Forex Support Vector Machine

Support Vector Machines Financial Applications. Listed dalam bentuk kutipan per tahun, tertinggi di atas. Diperbaharui baru-baru ini September 2006.PANG, Bo, Lillian LEE dan Shivakumar VAITHYANATHAN, 2002 Thumbs up Sentimen Klasifikasi menggunakan Teknik Belajar Mesin di EMNLP 02 Prosiding ACL -02 Konferensi Metode Empiris dalam Pengolahan Bahasa Alami - Volume 10 halaman 79--86 Dikutip oleh 154 36 66 tahun Abstrak Kami mempertimbangkan masalah penggolongan dokumen bukan berdasarkan topik, namun secara keseluruhan, misalnya menentukan apakah sebuah tinjauan positif atau negatif Dengan menggunakan ulasan film sebagai data, kami menemukan bahwa teknik pembelajaran mesin standar secara definitif mengungguli garis dasar yang diproduksi manusia. Namun, tiga metode pembelajaran mesin yang kami gunakan untuk Naive Bayes, klasifikasi entropi maksimum, dan mesin vektor pendukung tidak sesuai dengan klasifikasi sentimen seperti pada tradisi. Kategorisasi berbasis topik Kami menyimpulkan dengan memeriksa faktor-faktor yang membuat masalah klasifikasi sentimen mor Saya menantangnya, dengan menggunakan ulasan film sebagai data, teknik pembelajaran mesin standar secara definitif mengungguli garis dasar yang diproduksi manusia. Namun, mereka juga menemukan bahwa ketiga metode pembelajaran mesin yang mereka gunakan adalah Naive Bayes, klasifikasi entropi maksimum, dan mesin vektor pendukung tidak melakukan Baik pada klasifikasi sentimen seperti pada kategorisasi berbasis topik tradisional. VAN GESTEL, Tony, et al 2001 Prediksi Seri Waktu Keuangan Menggunakan Kuantitas Terkecil Kuantitas Vektor Mesin Dalam Kerangka Bukti IEEE Transaksi pada Jaringan Syaraf Tiruan Volume 12, Nomor 4, Juli 2001, Halaman 809 -821 Dikutip oleh 77 14 82 tahun Abstrak Kerangka bukti Bayesian diterapkan dalam makalah ini ke kuadrat terkecil yang mendukung regresi LS-SVM mesin vektor untuk menyimpulkan model nonlinear untuk memprediksi deret waktu keuangan dan volatilitas terkait Pada tingkat pertama kesimpulan , Kerangka statistik berhubungan dengan formulasi LS-SVM yang memungkinkan seseorang untuk memasukkan volume waktu yang bervariasi Atati pasar dengan pilihan yang tepat dari beberapa parameter hiper Parameter hiper dari model disimpulkan pada tingkat kedua inferensi Parameter hiper yang disimpulkan, yang terkait dengan volatilitas, digunakan untuk membangun model volatilitas dalam kerangka bukti Perbandingan model dilakukan pada tingkat ketiga inferensi untuk menyesuaikan parameter fungsi kernel secara otomatis dan untuk memilih input yang relevan Formulasi LS-SVM memungkinkan seseorang untuk memperoleh ekspresi analitik di ruang fitur dan ungkapan praktis diperoleh secara dual Ruang yang menggantikan produk dalam oleh fungsi kernel yang terkait dengan menggunakan teorema Mercer. Prediksi satu langkah di depan prediksi yang diperoleh pada prediksi tarif T-bill mingguan 90 hari dan harga penutupan DAX30 harian menunjukkan bahwa signifikansi dari prediksi tanda sampel dapat terjadi. Dibuat sehubungan dengan statistik uji Pesaran-Timmerman. Mengaplikasikan kerangka bukti Bayesian ke kuadrat terkecil untuk mendukung vecto. R mesin regresi LS-SVM untuk memprediksi tarif T-bill mingguan 90 hari dan harga penutupan harian DAX30.TAY, Francis EH dan Lijuan CAO, 2001 Penerapan mesin vektor pendukung dalam peramalan deret waktu keuangan Omega The International Journal of Management Science Volume 29, Edisi 4, Agustus 2001, Halaman 309-317 Dikutip oleh 67 12 89 tahun Abstrak Makalah ini membahas penerapan teknik jaringan syaraf baru, mendukung mesin vektor SVM, dalam peramalan deret waktu keuangan Tujuan makalah ini adalah untuk Memeriksa kelayakan SVM dalam peramalan deret waktu keuangan dengan membandingkannya dengan jaringan syaraf tiruan multi-lapisan belakang BP Lima kontrak futures futures yang dikumpulkan dari Chicago Mercantile Market digunakan sebagai kumpulan data Percobaan menunjukkan bahwa SVM melebihi BP Jaringan syaraf tiruan berdasarkan kriteria normal mean square error NMSE, mean absolut error MAE, directional simetry DS dan simetri directional tertimbang WDS Karena ada n O cara terstruktur untuk memilih parameter SVM yang bebas, variabilitas dalam kinerja berkenaan dengan parameter bebas diselidiki dalam penelitian ini Analisis hasil eksperimen membuktikan bahwa sangat menguntungkan untuk menerapkan SVM untuk meramalkan deret waktu keuangan. Melihat bahwa SVM mengungguli Jaringan syaraf tiruan multi-layer back-propagation pada lima kontrak berjangka nyata dari Chicago Mercantile Market. TAY, Francis EH dan LJ CAO, 2002 Mesin vektor pendukung modifikasi dalam peramalan seri waktu keuangan Neurocomputing Volume 48, Isu 1-4, Oktober 2002 , Halaman 847-861 Dikutip oleh 54 12 86 tahun Abstrak Makalah ini mengusulkan versi modifikasi dari mesin vektor pendukung, yang disebut C - ascending support vector machine, untuk memodelkan rangkaian waktu keuangan non-stasioner Mesin vektor pendukung C-gain diperoleh oleh Modifikasi sederhana dari fungsi risiko reguler dalam mendukung mesin vektor, dimana kesalahan sensitif baru-baru ini dikenakan sanksi lebih berat daripada yang jauh - i Nsensitive errors Prosedur ini didasarkan pada pengetahuan sebelumnya bahwa dalam rangkaian waktu keuangan non-stasioner, ketergantungan antara variabel input dan variabel output berangsur-angsur berubah dari waktu ke waktu, secara khusus, data terakhir ini dapat memberikan informasi yang lebih penting daripada data masa lalu yang jauh. Percobaan, mesin vektor pendukung C-gain dikembangkan dengan menggunakan tiga futures nyata yang dikumpulkan dari Chicago Mercantile Market. Hal ini menunjukkan bahwa mesin vektor pendukung C-retriending dengan data sampel yang benar-benar dipesan secara konsisten diperkirakan lebih baik daripada mesin vektor pendukung standar, dengan Kinerja terburuk ketika data sampel yang digunakan secara terbalik digunakan Selanjutnya, mesin vektor pendukung C-gain menggunakan vektor pendukung lebih sedikit daripada mesin vektor pendukung standar, yang menghasilkan representasi sparser dari mesin pengembangan pendukung C - ascending solutiondeveloped, yang menghukum Kesalahan sensitif baru-baru ini lebih banyak daripada jauh - insensit Kesalahan ive, dan menemukan bahwa mereka meramalkan lebih baik daripada SVM standar pada tiga real futures yang dikumpulkan dari Chicago Mercantile Market. HUANG, Zan, et al 2004 Analisis peringkat kredit dengan mesin vektor pendukung dan jaringan syaraf tiruan studi perbandingan pasar Sistem Pendukung Keputusan Volume 37, Edisi 4 September 2004, Halaman 543-558 Dikutip oleh 21 9 55 tahun Abstrak Analisis peringkat kredit perusahaan telah menarik banyak minat penelitian dalam literatur Studi terbaru menunjukkan bahwa metode AI Kecerdasan Buatan mencapai kinerja yang lebih baik daripada metode statistik tradisional Artikel ini memperkenalkan sebuah pendekatan yang relatif Teknik mesin baru, mendukung mesin vektor SVM, hingga masalah dalam usaha memberikan model dengan kekuatan penjelas yang lebih baik. Kami menggunakan jaringan syaraf tiruan BNN sebagai tolok ukur dan memperoleh akurasi prediksi sekitar 80 untuk metode BNN dan SVM untuk Amerika Serikat dan Taiwan. Pasar Namun, hanya sedikit peningkatan SVM yang diamati arah lain Dari penelitian ini adalah untuk memperbaiki interpretasi model berbasis AI Kami menerapkan hasil penelitian terbaru dalam interpretasi model jaringan syaraf tiruan dan memperoleh relatif pentingnya variabel keuangan masukan dari model jaringan syaraf Berdasarkan hasil ini, kami melakukan analisis komparatif pasar pada Perbedaan faktor penentu di pasar Amerika Serikat dan Taiwan. Mengaplikasikan jaringan syaraf tiruan dan SVM kepada prediksi pemeringkatan kredit korporat untuk pasar Amerika Serikat dan Taiwan dan menemukan bahwa hasilnya sebanding keduanya lebih unggul dari regresi logistik, dengan SVM sedikit lebih baik..CAO, Lijuan, 2003 Mendukung ahli mesin vektor untuk peramalan seri waktu Neurocomputing Volume 51, April 2003, Halaman 321-339 Dikutip dari 29 9 08 tahun Abstrak Makalah ini mengusulkan menggunakan mesin vektor pendukung ahli SVM untuk peramalan deret waktu Para ahli SVM umum Memiliki arsitektur jaringan syaraf dua tahap Pada tahap pertama, self-organi Peta fitur zing SOM digunakan sebagai algoritma clustering untuk mempartisi keseluruhan ruang masukan ke beberapa daerah yang tidak terputus Arsitektur pohon-struktur diadopsi di partisi untuk menghindari masalah penentuan jumlah daerah yang dipartisi. Kemudian, pada tahap kedua, beberapa SVM , Juga disebut pakar SVM, wilayah yang dipartisi paling baik dibangun dengan menemukan fungsi kernel yang paling tepat dan parameter SVM yang bebas optimal. Data titik matahari, data Santa Fe menetapkan A, C dan D, dan dua kumpulan data bangunan dievaluasi dalam Percobaan Simulasi menunjukkan bahwa para ahli SVM mencapai peningkatan yang signifikan dalam kinerja generalisasi dibandingkan dengan model SVM tunggal. Selain itu, para ahli SVM juga bertemu lebih cepat dan menggunakan lebih sedikit vektor pendukung. Mengingat metode ahli SVM mereka mencapai peningkatan yang signifikan di atas satu Model SVM ketika diterapkan pada data Santa Fe menetapkan C frekuensi tinggi nilai tukar antara Swiss franc dan t Dia dolar AS. KIM, Kyoung-jae, 2003 Peramalan waktu keuangan menggunakan mesin vektor pendukung Neurocomputing Volume 55, Isu 1-2 September 2003, Halaman 307-319 Dikutip oleh 28 8 76 tahun Abstrak Mesin vektor pendukung SVM adalah metode yang menjanjikan untuk Prediksi deret waktu keuangan karena mereka menggunakan fungsi risiko yang terdiri dari kesalahan empiris dan istilah regularized yang berasal dari prinsip minimisasi risiko struktural. Penelitian ini menggunakan SVM untuk memprediksi indeks harga saham. Selain itu, penelitian ini menguji kelayakan penerapan SVM dalam peramalan keuangan dengan membandingkannya dengan jaringan syaraf tiruan balik dan penalaran berbasis kasus Hasil eksperimen menunjukkan bahwa SVM memberikan alternatif yang menjanjikan untuk prediksi pasar saham. Mengetahui bahwa SVM mengungguli jaringan syaraf tiruan belakang dan penalaran berbasis kasus bila digunakan untuk Meramalkan indeks harga saham komposit Korea harian KOSPI. SHIN Kyung-Shik, Taik Soo LEE dan Hyun-jung KIM, 2005 Sebuah aplikasi Pada mesin vektor pendukung dalam model prediksi kebangkrutan Sistem Pakar dengan Aplikasi Volume 28, Issue 1, Januari 2005, Halaman 127-135 Dikutip oleh 8 6 67 tahun Abstrak Penelitian ini menguji efikasi penerapan mesin vektor SVM pendukung terhadap masalah prediksi kebangkrutan walaupun Adalah fakta yang diketahui bahwa jaringan syaraf tiruan balik BPN berkinerja baik dalam tugas pengenalan pola, metode ini memiliki beberapa keterbatasan karena merupakan seni untuk menemukan struktur model dan solusi optimal yang tepat. Selain itu, memuat sebanyak set pelatihan Mungkin ke dalam jaringan diperlukan untuk mencari bobot jaringan Di sisi lain, karena SVM menangkap karakteristik geometrik ruang fitur tanpa menurunkan bobot jaringan dari data pelatihan, ia mampu mengeluarkan solusi optimal dengan set pelatihan kecil. Size Dalam penelitian ini, kami menunjukkan bahwa classifier yang diusulkan dari pendekatan SVM melebihi BPN terhadap masalah prediksi kebangkrutan perusahaan. Hasil penelitian menunjukkan bahwa akurasi dan generalisasi kinerja SVM lebih baik daripada BPN karena ukuran set pelatihan semakin kecil. Kami juga memeriksa pengaruh variabilitas kinerja terhadap berbagai parameter parameter di SVM. Selain itu, kami menyelidiki dan meringkas Beberapa poin superior algoritma SVM dibandingkan dengan BPN. demonstrasi bahwa SVM berperforma lebih baik daripada jaringan syaraf tiruan belakang saat diterapkan pada prediksi kebangkrutan perusahaan. CAO, LJ dan Francis EH TAY, 2003 Mendukung Mesin Vektor dengan Parameter Adaptif dalam Peramaman Seri Waktu Keuangan Transaksi IEEE pada Jaringan Syaraf Tiruan Volume 14, Edisi 6, November 2003, Halaman 1506-1518 Dikutip dari 20 6 25 tahun Abstrak Jenis mesin pembelajaran baru yang disebut SVM mesin pendukung SVM telah menerima minat yang semakin meningkat di berbagai bidang mulai dari penerapan aslinya dalam pola. Pengakuan terhadap aplikasi lain seperti estimasi regresi karena perfo generalisasi yang luar biasa Rmance Makalah ini membahas penerapan SVM dalam peramalan deret waktu keuangan Kelayakan penerapan SVM dalam peramalan keuangan pertama-tama dikaji dengan membandingkannya dengan jaringan syaraf tiruan back-propagation multilayer dan fungsi dasar radial yang teratur Jaringan syaraf RBF Variabilitas dalam kinerja SVM berkenaan dengan parameter bebas diselidiki secara eksperimental Parameter adaptif kemudian diajukan dengan memasukkan nonduktivitas deret waktu keuangan ke SVM Lima kontrak berjangka nyata yang disusun dari Chicago Mercantile Market digunakan sebagai kumpulan data Simulasi menunjukkan bahwa di antara ketiga metode tersebut , SVM mengungguli jaringan saraf syaraf dalam peramalan keuangan, dan ada kinerja generalisasi yang sebanding antara SVM dan jaringan saraf RBF yang diatur sebelumnya. Parameter bebas SVM memiliki pengaruh yang besar terhadap kinerja umum SVM dengan parameter adaptif keduanya dapat mencapai kinerja generalisasi yang lebih tinggi. Dan menggunakan lebih sedikit vektor pendukung daripada standar SVM dalam peramalan keuangan. Menggunakan SVM, jaringan syaraf tiruan BP back-propagation multilayer dan fungsi dasar radial rutin Jaringan syaraf RBF untuk memprediksi lima kontrak berjangka nyata yang dikumpulkan dari Chicago Mercantile Market Results menunjukkan bahwa SVM dan jaringan syaraf tiruan RBF yang disesuaikan sebanding dan keduanya mengungguli jaringan saraf BP. CAO, Lijuan dan Francis EH TAY, Peramalan Keuangan 2001 Menggunakan Mesin Vektor Pendukung Aplikasi Komputasi Neural Volume 10, Nomor 2 Mei 2001, Halaman 184-192 Dikutip oleh 26 5 00 tahun Abstrak Penggunaan Mesin Vektor Pendukung SVM dipelajari pada peramalan keuangan dengan membandingkannya dengan perceptron multi layer yang dilatih oleh algoritma Back Propagation BP perkiraan SVM lebih baik daripada BP berdasarkan kriteria Normalized Mean Square Error NMSE, Mean Absolute Kesalahan MAE, Directional Symmetry DS, tren Up CP yang benar dan tren Down Down yang benar Indeks harga harian SP 500 digunakan sebagai d Ata set Karena tidak ada cara terstruktur untuk memilih parameter SVM yang bebas, kesalahan generalisasi berkenaan dengan parameter SVM yang bebas diselidiki dalam percobaan ini. Seperti yang digambarkan dalam eksperimen, dampaknya kecil terhadap solusi Analisis hasil eksperimen Menunjukkan bahwa sangat menguntungkan untuk menerapkan SVM untuk meramalkan deret waktu keuangan. Melihat bahwa SVM memperkirakan indeks harga harian SP 500 lebih baik daripada perceptron multi layer yang dilatih oleh algoritma BP Propagasi Balik. M, Jae H dan Young-Chan LEE, 2005 Prediksi kebangkrutan dengan menggunakan mesin vektor pendukung dengan pilihan parameter fungsi kernel yang optimal Sistem Pakar dengan Aplikasi Volume 28, Edisi 4, Mei 2005, Halaman 603-614 Dikutip oleh 6 5 00 tahun Abstrak Prediksi kebangkrutan telah menarik banyak minat penelitian pada literatur sebelumnya. , Dan penelitian terbaru menunjukkan bahwa teknik pembelajaran mesin mencapai kinerja yang lebih baik daripada statistik tradisional. Makalah ini menerapkan dukungan Mesin vektor SVM ke masalah prediksi kebangkrutan dalam upaya untuk menyarankan model baru dengan kekuatan dan stabilitas penjelasan yang lebih baik Untuk mencapai tujuan ini, kami menggunakan teknik pencarian grid dengan menggunakan validasi silang 5 kali lipat untuk mengetahui nilai parameter kernel yang optimal. Fungsi SVM Selain itu, untuk mengevaluasi akurasi prediksi SVM, kita membandingkan kinerjanya dengan analisis multiple diskriminan MDA, Logistic Log Regression Logit, dan jaringan syaraf tiruan back-propagation tiga lapis yang terhubung sepenuhnya BPN Hasil eksperimen menunjukkan bahwa SVM lebih baik daripada kinerja sebelumnya. Metode lain. Jika demikian, ketika diterapkan pada prediksi kebangkrutan, SVM mengungguli beberapa analisis diskriminan MDA, analisis regresi logistik Logit dan jaringan syaraf tiruan back-propagation tiga lapis yang terhubung sepenuhnya BPNs. ABRAHAM, Ajith, Ninan Sajith PHILIP dan P SARATCHANDRAN, 2003 Pemodelan Perilaku kacau indeks saham menggunakan paradigma cerdas Neural, Paralel Scientific Computations Volume 11, page S 143-160 Dikutip oleh 10 4 55 tahun Abstrak Penggunaan sistem cerdas untuk prediksi pasar saham telah banyak dikembangkan. Dalam tulisan ini, kami menyelidiki bagaimana perilaku pasar saham yang tampaknya kacau dapat diwakili dengan baik dengan menggunakan beberapa paradigma connectionist dan teknik komputasi lunak. Untuk mendemonstrasikan teknik yang berbeda, kami mempertimbangkan indeks Nasdaq-100 Nasdaq Stock Market SM dan nilai indeks utama S Nasdaq 100 dan nilai indeks NIFTY 4 tahun. Makalah ini menyelidiki pengembangan teknik yang andal dan efisien untuk memodelkan perilaku yang tampaknya kacau. Dari pasar saham Kami menganggap jaringan syaraf tiruan yang dilatih menggunakan algoritma Levenberg-Marquardt, Support Vector Machine SVM, model neurofuzzy Takagi-Sugeno dan Jaringan Syaraf Tiruan Perbedaan Bilangan DBNN Makalah ini secara singkat menjelaskan bagaimana paradigma connectionist yang berbeda dapat diformulasikan dengan menggunakan metode pembelajaran yang berbeda dan Kemudian menyelidiki apakah mereka dapat memberikan tingkat yang diperlukan per Kinerja yang cukup baik dan kuat sehingga dapat memberikan model perkiraan yang dapat diandalkan untuk indeks pasar saham Hasil percobaan menunjukkan bahwa semua paradigma connectionist dianggap dapat mewakili perilaku indeks saham dengan sangat akurat. Dengan menerapkan empat teknik yang berbeda, jaringan syaraf tiruan yang terlatih menggunakan Algoritma Levenberg-Marquardt, mesin vektor pendukung, perbedaan yang meningkatkan jaringan syaraf tiruan dan sistem inferensi fuzzy Takagi-Sugeno belajar menggunakan algoritma neural-fuzzy model neural dengan prediksi indeks Nasdaq-100 Nasdaq Stock Market dan S - Descending Support Vector Machines untuk Financial Time Series Forecasting Neural Processing Letters 15 2 179-195 Dikutip oleh 11 2 62 tahun Abstrak Makalah ini mengusulkan versi modifikasi dari mesin vektor SVM pendukung, yang disebut dengan mesin vektor pendukung - DSVM, untuk model non-stasioner Time series keuangan The - DSVMs diperoleh dengan memasukkan pengetahuan domain bermasalah non-stationa Tingkat waktu keuangan menjadi SVM Tidak seperti SVM standar yang menggunakan tabung konstan di semua titik data pelatihan, - DSVM menggunakan tabung adaptif untuk menangani perubahan struktur dalam percobaan menunjukkan bahwa - DSVMs menggeneralisasi lebih baik daripada SVM standar. Dalam meramalkan seri waktu keuangan non-stasioner Keuntungan lain dari modifikasi ini adalah bahwa - DSVMs bertemu dengan vektor pendukung yang lebih sedikit, menghasilkan representasi solusi yang jarang. Mengaitkan pengetahuan domain masalah tentang non-stasioneritas time series keuangan ke SVM dengan menggunakan Sebuah tabung adaptif dalam mesin epsilon - descending support epsilon yang mereka sebut epsilon - DSVMs Percobaan menunjukkan bahwa epsilon - DSVMs menggeneralisasi lebih baik daripada SVM standar dalam meramalkan rangkaian waktu keuangan non-stasioner dan juga bertemu dengan vektor pendukung yang lebih sedikit, menghasilkan representasi yang jarang dari Solusi. DEBNATH, Sandip dan C Lee GILES, 2005 Model Pembelajaran Berbasis Ekstraksi Judul Artikel Berita t O Cari Kalimat Penjelasan untuk Acara di K-CAP 05 Prosiding konferensi internasional ke-3 mengenai Knowledge capture Halaman 189--190 Dikutip oleh 2 1 67 tahun Abstrak Informasi metadata memainkan peran penting dalam menambah dokumen yang mengatur efisiensi dan kearsipan Metadata berita termasuk DateLine ByLine Headline dan banyak lainnya Kami menemukan bahwa informasi Headline berguna untuk menebak tema artikel berita Khusus untuk artikel berita keuangan, kami menemukan bahwa Headline dapat membantu secara khusus untuk menemukan kalimat jelas untuk setiap peristiwa besar seperti perubahan harga saham yang signifikan. Makalah ini kami mengeksplorasi pendekatan pembelajaran berbasis vektor pendukung untuk mengekstrak metadata Headline secara otomatis. Kami menemukan bahwa keakuratan klasifikasi untuk mengetahui peningkatan Headline akan meningkat jika DateLine diidentifikasi terlebih dahulu. Kami kemudian menggunakan Headline yang diekstraksi untuk memulai pencocokan pola kata kunci untuk ditemukan. Kalimat yang bertanggung jawab untuk tema cerita Menggunakan tema ini dan yang sederhana Model bahasa adalah mungkin untuk menemukan kalimat jelas untuk setiap perubahan harga yang signifikan. Dia memikirkan pendekatan baru untuk mengekstrak metadata berita Headline menggunakan SVM dan menggunakannya untuk menemukan tema cerita untuk mendapatkan penjelasan berbasis kalimat untuk perubahan harga saham. Van GESTEL, Tony, et al 2003 Pendekatan mesin vektor dukungan terhadap penilaian kredit Bank en Financiewezen Volume 2, March, Halaman 73-82 Dikutip oleh 5 1 56 tahun Abstrak Didorong oleh kebutuhan untuk mengalokasikan modal dengan cara yang menguntungkan dan dengan Basel II yang baru disarankan Peraturan, lembaga keuangan semakin berkewajiban untuk membangun model penilaian kredit yang menilai risiko kegagalan klien mereka Banyak teknik yang disarankan untuk mengatasi masalah ini Dukungan Mesin Vektor SVM adalah teknik baru yang menjanjikan yang baru-baru ini berasal dari berbagai domain seperti Statistik terapan, jaringan syaraf tiruan dan pembelajaran mesin Dalam tulisan ini, kami bereksperimen dengan kuadrat terkecil yang mendukung mesin vektor LS-SVMs, yang baru-baru ini Versi SVM yang dimodifikasi, dan melaporkan hasil yang lebih baik secara signifikan bila dibandingkan dengan teknik klasik yang mensponsori empat metodologi, OLS Ordinary Least Squares OLS, OLR Regresi Logistik Ordinal, MLP Multilayer Perceptron dan kuadrat terkecil mendukung mesin vektor LS-SVM saat diterapkan pada penilaian kredit Metodologi SVM Menghasilkan hasil yang signifikan dan konsisten lebih baik daripada metode penilaian linier klasik. FAN, Alan dan Marimuthu PALANISWAMI, 2000 Memilih Prediktor Bangkrut Menggunakan Mesin Pendukung Mesin Vektor Pendekatan IJCNN 2000 Prosiding Konferensi Gabungan Internasional IEEE-INNS-ENNS tentang Jaringan Syaraf Tiruan, Volume 6 diedit Oleh Shun-Ichi Amari dkk hal 6354 Dikutip oleh 9 1 45 tahun Abstrak Pendekatan Neural Network Konvensional telah ditemukan berguna dalam memprediksi tekanan perusahaan dari laporan keuangan. Dalam tulisan ini, kami telah mengadopsi pendekatan Support Vector Machine untuk masalah ini. Memilih prediktor kebangkrutan ditunjukkan, menggunakan jarak Euclidean Berdasarkan kriteria yang dihitung dalam kernel SVM Sebuah studi komparatif diberikan dengan menggunakan tiga model distress korporat klasik dan sebuah model alternatif berdasarkan pendekatan SVM. Gunakan SVM untuk memilih prediktor kebangkrutan, dan berikan sebuah studi perbandingan. HARI, Francis Eng Hock dan Li Juan CAO , 2001 Peningkatan peramalan deret waktu keuangan dengan menggabungkan Support Vector Machines dengan peta fitur self-organizing Analisis Data Cerdas Volume 5, Nomor 4, Halaman 339-354 Dikutip oleh 7 1 35 tahun Abstrak Arsitektur jaringan syaraf dua tahap dibangun dengan menggabungkan Support Vector Mesin SVM dengan peta fitur pengorganisasian diri SOM diusulkan untuk peramalan deret waktu keuangan Pada tahap pertama, SOM digunakan sebagai algoritma pengelompokan untuk mempartisi keseluruhan ruang masukan ke beberapa daerah yang terpisah. Arsitektur pohon terstruktur diadopsi di partisi untuk menghindari Masalah penentuan jumlah daerah yang dipartisi Kemudian, pada tahap kedua, beberapa SVM, disebut juga SVM exper Ts, yang paling sesuai dengan masing-masing wilayah yang dipartisi dibangun dengan menemukan fungsi kernel yang paling tepat dan parameter pembelajaran SVM yang optimal. Nilai tukar Santa Fe dan lima kontrak berjangka nyata digunakan dalam percobaan. Hal ini menunjukkan bahwa metode yang diusulkan mencapai keduanya secara signifikan lebih tinggi. Kinerja prediksi dan kecepatan konvergensi yang lebih cepat dibandingkan dengan SVM model SVMs tunggal dengan siri fitur self-organizing SOM dan menguji model pada nilai tukar Santa Fe dan lima kontrak futures nyata Mereka menunjukkan bahwa metode yang diusulkan mereka mencapai kinerja prediksi yang jauh lebih tinggi dan Kecepatan konvergensi yang lebih cepat dibandingkan dengan model SVM tunggal. SANSOM, DCT DOWNS dan TK SAHA, 2003 Evaluasi alat peramalan mesin berbasis vektor yang mendukung peramalan harga listrik untuk peserta pasar listrik nasional Australia Jurnal indeks harga saham PX CNX Inti paradigma cerdas dipertimbangkan Adalah jaringan syaraf tiruan t Hujan menggunakan algoritma Levenberg-Marquardt, mesin vektor pendukung, model neuro-fuzzy Takagi-Sugeno dan perbedaan yang meningkatkan jaringan syaraf. Berbagai paradigma digabungkan menggunakan dua pendekatan ansambel yang berbeda sehingga dapat mengoptimalkan kinerjanya dengan mengurangi berbagai kesalahan pengukuran. Pendekatan pertama adalah Berdasarkan pengukuran kesalahan langsung dan metode kedua didasarkan pada algoritma evolusioner untuk mencari kombinasi linear optimal dari paradigma cerdas yang berbeda. Hasil eksperimen menunjukkan bahwa teknik ensemble yang dilakukan lebih baik daripada metode individual dan pendekatan ansambel langsung tampaknya berjalan dengan baik untuk Masalah yang dipertimbangkan. Memikirkan sebuah jaringan syaraf tiruan yang dilatih menggunakan algoritma Levenberg-Marquardt, mesin vektor pendukung, model neuro-fuzzy Takagi-Sugeno dan perbedaan yang mendorong jaringan syaraf tiruan untuk memprediksi Indeks NASDAQ-100 Pasar Saham Nasdaq dan S REZ-CRUZ, Fernando, Julio A AFONSO-RODR GUEZ dan Javier GINER, 2003 Esti Kawin model GARCH menggunakan mesin vektor pendukung Kuantitatif Keuangan Volume 3, Nomor 3 Juni 2003, Halaman 163-172 Dikutip oleh 2 0 63 tahun Abstrak Mesin vektor pendukung SVM adalah alat nonparametrik baru untuk estimasi regresi Kami akan menggunakan alat ini untuk memperkirakan parameter dari Model GARCH untuk memprediksi volatilitas bersyarat dari return pasar saham Model GARCH biasanya diperkirakan menggunakan prosedur ML likelihood maksimum, dengan asumsi bahwa data terdistribusi secara normal. Dalam tulisan ini, kami akan menunjukkan bahwa model GARCH dapat diestimasi dengan menggunakan SVM dan perkiraan tersebut Sebuah kemampuan prediksi yang lebih tinggi daripada yang diperoleh melalui metode ML yang umum. Menggunakan SVM untuk regresi untuk memperkirakan parameter model GARCH untuk memprediksi volatilitas bersyarat dari tingkat pengembalian pasar saham dan menunjukkan bahwa perkiraan tersebut memiliki kemampuan prediksi lebih tinggi daripada yang diperoleh melalui kemungkinan maksimum yang umum. Metode ML. Van GESTEL, T et al 2003 Prediksi kebangkrutan dengan vektor kuadrat terkecil mendukung m Pengelompokan achine Pada tahun 2003 Konferensi Internasional IEEE tentang Kecerdasan Komputasi untuk Prosiding Rekayasa Keuangan halaman 1-8 Dikutip oleh 2 0 63 tahun Abstrak Algoritma klasifikasi seperti analisis diskriminan linier dan regresi logistik adalah teknik linier yang populer untuk pemodelan dan prediksi tekanan perusahaan Teknik ini bertujuan untuk menemukan Kombinasi linear yang optimal dari variabel input penjelasan, seperti rasio solvabilitas dan likuiditas, untuk menganalisa, memodelkan dan memprediksi risiko default perusahaan. Baru-baru ini, teknik klasifikasi nonlinier berbasis kernel, seperti mesin vektor pendukung, kuadrat terkecil yang mendukung mesin vektor dan kernel fisher. Analisis diskriminan, telah dikembangkan Pada dasarnya, metode ini memetakan masukan terlebih dahulu dengan cara nonlinier ke ruang fitur induksi kernel yang diinduksi dimensi tinggi, di mana pengklasifikasi linear dibangun pada langkah kedua Ekspresi praktis diperoleh dalam ruang ganda yang disebut Dengan aplikasi Mercer s Orem Dalam makalah ini, kami menjelaskan hubungan antara klasifikasi linier linier dan nonlinier dan menggambarkan kinerjanya dalam memprediksi kebangkrutan perusahaan mid-cap di Belgia dan Belanda. Sebagian besar kuadratnya mendukung pengelompokan mesin vektor untuk memprediksi kebangkrutan perusahaan mid-cap di Belgia dan Belanda. CAO, LJ dan WK CHONG, 2002 Ekstraksi fitur dalam mendukung mesin vektor Perbandingan PCA, XPCA dan ICA ICONIP 02 Prosiding Konferensi Internasional ke-9 tentang Pengolahan Informasi Neural, Volume 2 diedit oleh Lipo Wang, dkk. 1001-1005 Dikutip oleh 2 0 48 tahun Abstrak Baru-baru ini, dukungan mesin vektor SVM telah menjadi alat yang populer dalam peramalan time series Dalam mengembangkan peramal SVM yang sukses, ekstraksi fitur merupakan langkah penting pertama Makalah ini mengusulkan penerapan analisis komponen utama PCA, Analisis komponen utama kernel KPCA dan analisis komponen independen ICA terhadap SVM untuk ekstraksi fitur PCA secara linear Sforms input asli ke dalam fitur yang tidak berkorelasi KPCA adalah PCA nonlinier yang dikembangkan dengan menggunakan metode kernel Di ICA, input asli diubah secara linear menjadi fitur independen secara statistik Dengan memeriksa data titik matahari dan satu kontrak berjangka nyata, percobaan menunjukkan bahwa SVM dengan ekstraksi fitur Dengan menggunakan PCA, KPCA atau ICA dapat berkinerja lebih baik daripada tanpa ekstraksi fitur. Selain itu, ada kinerja generalisasi yang lebih baik dalam ekstraksi fitur KPCA dan ICA daripada ekstraksi fitur PCA. Mempertimbangkan penerapan analisis komponen utama PCA, analisis komponen utama kernel KPCA dan analisis komponen independen. ICA ke SVM untuk ekstraksi fitur Dengan memeriksa data titik matahari dan satu kontrak berjangka nyata, mereka menunjukkan bahwa SVM dengan ekstraksi fitur menggunakan PCA, KPCA atau ICA dapat berkinerja lebih baik daripada tanpa ekstraksi fitur. Selain itu, mereka menemukan bahwa ada kinerja generalisasi yang lebih baik di KPCA Dan ekstraksi fitur ICA dibanding fitur PCA E ekstraksi. CAO, LJ dan Francis EH TAY, 2000 Seleksi Fitur untuk Mesin Vektor Dukungan dalam Peramalan Seri Waktu Keuangan dalam Teknik Data Cerdas dan Pembelajaran Otomatis - Data Mining 2000, Data Mining, dan Intelligent Agents yang diedit oleh Kwong Sak Leung, Lai - Wan Chan dan Helen Meng, halaman 268-273 Dikutip oleh 3 0 48 tahun Abstrak Makalah ini membahas penerapan analisis saliency untuk Mendukung Mesin Vektor SVM untuk pemilihan fitur Pentingnya fitur tersebut diberi peringkat dengan mengevaluasi sensitivitas output jaringan ke Masukan fitur dalam hal turunan parsial Pendekatan sistematis untuk menghilangkan fitur yang tidak relevan berdasarkan sensitivitas dikembangkan Lima kontrak futures diperiksa dalam eksperimen Berdasarkan hasil Simulasi, ditunjukkan bahwa analisis saliency efektif dalam SVM untuk mengidentifikasi fitur penting..dealt dengan penerapan analisis saliency untuk memilih fitur untuk SVM Lima kontrak berjangka diperiksa Ed dan mereka menyimpulkan bahwa analisis saliency efektif dalam SVM untuk mengidentifikasi fitur penting. ZHOU, Dianmin, Feng GAO dan Xiaohong GUAN, 2004 Penerapan regresi vektor regresi online akurat dalam perkiraan harga energi WCICA 2004 Kongres Dunia Kelima tentang Kontrol dan Otomasi Cerdas, Volume 2 halaman 1838-1842 Dikutip oleh 1 0 45 tahun Abstrak Harga energi merupakan indikator terpenting di pasar tenaga listrik dan karakteristiknya terkait dengan mekanisme pasar dan perubahan terhadap perilaku pelaku pasar. Perlu dibangun harga real-time. forecasting model with adaptive capability In this paper, an accurate online support vector regression AOSVR method is applied to update the price forecasting model Numerical testing results show that the method is effective in forecasting the prices of the electric-power markets. applied an accurate online support vector regression AOSVR to forecasting the prices of the electric-power markets, results showed t hat it was effective. FAN, A and M PALANISWAMI, 2001 Stock selection using support vector machines IJCNN 01 International Joint Conference on Neural Networks, Volume 3 Pages 1793-1798 Cited by 2 0 38 year Abstract We used the support vector machines SVM in a classification approach to beat the market Given the fundamental accounting and price information of stocks trading on the Australian Stock Exchange, we attempt to use SVM to identify stocks that are likely to outperform the market by having exceptional returns The equally weighted portfolio formed by the stocks selected by SVM has a total return of 208 over a five years period, significantly outperformed the benchmark of 71 We also give a new perspective with a class sensitivity tradeoff, whereby the output of SVM is interpreted as a probability measure and ranked, such that the stocks selected can be fixed to the top 25.used SVMs for classification for stock selection on the Australian Stock Exchange and significantly outperformed the benchmark. Van GESTEL, Tony, et al 2000 Volatility Tube Support Vector Machines Neural Network World vol 10, number 1, pp 287-297 Cited by 2 0 32 year Abstract In Support Vector Machines SVM s , a non-linear model is estimated based on solving a Quadratic Programming QP problem The quadratic cost function consists of a maximum likelihood cost term with constant variance and a regularization term By specifying a difference inclusion on the noise variance model, the maximum likelihood term is adopted for the case of heteroskedastic noise, which arises in financial time series The resulting Volatility Tube SVM s are applied on the 1-day ahead prediction of the DAX30 stock index The influence of today s closing prices of the New York Stock Exchange on the prediction of tomorrow s DAX30 closing price is analyzed. developed the Volatility Tube SVM and applied it to 1-day ahead prediction of the DAX30 stock index, and significant positive out-of-sample results were obtained. CAO, Li Juan, K ok Seng CHUA and Lim Kian GUAN, 2003 Combining KPCA with support vector machine for time series forecasting In 2003 IEEE International Conference on Computational Intelligence for Financial Engineering pages 325-329 Cited by 1 0 31 year Abstract Recently, support vector machine SVM has become a popular tool in time series forecasting In developing a successful SVM forecaster, the first important step is feature extraction This paper applies kernel principal component analysis KPCA to SVM for feature extraction KPCA is a nonlinear PCA developed by using the kernel method It firstly transforms the original inputs into a high dimensional feature space and then calculates PCA in the high dimensional feature space By examining the sunspot data and one real futures contract, the experiment shows that SVM by feature forms much better than that extraction using KPCA per without feature extraction In comparison with PCA, there is also superior performance in KPCA. applied kernel principal compon ent analysis KPCA to SVM for feature extraction The authors examined sunspot data and one real futures contract, and found such feature extraction enhanced performance and also that KPCA was superior to PCA. YANG, Haiqin, 2003 Margin Variations in Support Vector Regression for the Stock Market Prediction Degree of Master of Philosophy Thesis, Department of Computer Science - insensitive loss function is usually used to measure the empirical risk The margin in this loss function is fixed and symmetrical Typically, researchers have used methods such as crossvalidation or random selection to select a suitable for that particular data set In addition, financial time series are usually embedded with noise and the associated risk varies with time Using a fixed and symmetrical margin may have more risk inducing bad results and may lack the ability to capture the information of stock market promptly In order to improve the prediction accuracy and to consider reducing the downside risk, we extend the standard SVR by varying the margin By varying the width of the margin, we can reflect the change of volatility in the financial data by controlling the symmetry of margins, we are able to reduce the downside risk Therefore, we focus on the study of setting the width of the margin and also the study of its symmetry property For setting the width of margin, the Momentum also including asymmetrical margin control and Generalized Autoregressive Conditional Heteroskedasticity GARCH models are considered Experiments are performed on two indices Hang Seng Index HSI and Dow Jones Industrial Average DJIA for the Momentum method and three indices Nikkei225, DJIA and FTSE100, for GARCH models, respectively The experimental results indicate that these methods improve the predictive performance comparing with the standard SVR and benchmark model On the study of the symmetry property, we give a sufficient condition to prove that the predicted value is monotone decreasing to the increase of the up margin Therefore, we can reduce the predictive downside risk, or keep it zero, by increasing the up margin An algorithm is also proposed to test the validity of this condition, such that we may know the changing trend of predictive downside risk by only running this algorithm on the training data set without performing actual prediction procedure Experimental results also validate our analysis. employs SVMs for regression and varys the width of the margin to reflect the change of volatility and controls the symmetry of margins to reduce the downside risk Results were positive. CALVO, Rafael A and Ken WILLIAMS, 2002 Automatic Categorization of Announcements on the Australian Stock Exchange Cited by 1 0 24 year Abstract This paper compares the performance of several machine learning algorithms for the automatic categorization of corporate announcements in the Australian Stock Exchange ASX Signal G data stream The article also describes some of the applications that the categorization of corporate announcements may enable We have performed tests on two categorization tasks market sensitivity, which indicates whether an announcement will have an impact on the market, and report type, which classifies each announcement into one of the report categories defined by the ASX We have tried Neural Networks, a Na ve Bayes classifier, and Support Vector Machines and achieved good resultspared the performance of neural networks, a na ve bayes classifier, and SVMs for the automatic categorization of corporate announcements in the Australian Stock Exchange ASX Signal G data stream The results were all good, but with the SVM underperforming the other two models. AHMED, A H M T 2000 Forecasting of foreign exchange rate time series using support vector regression 3rd year project Computer Science Department, University of Manchester Cited by 1 0 16 year. used support vector regression for forecasting a foreign exchange rate time series. GUESDE, Bazile, 2000 Predicting foreign exchange r ates with support vector regression machines MSc thesis Computer Science Department, University of Manchester Cited by 1 0 16 year Abstract This thesis investigates how Support Vector Regression can be applied to forecasting foreign exchange rates At first we introduce the reader to this non linear kernel based regression and demonstrate how it can be used for time series prediction Then we define a predictive framework and apply it to the Canadian exchange rates But the non-stationarity in the data, which we here define as a drift in the map of the dynamics, forces us to present and use the typical learning processes for catching different dynamics Our implementation of these solutions include Clusters of Volatility and competing experts Finally those experts are used in a financial vote trading system and substantial profits are achieved Through out the thesis we hope the reader will be intrigued by the results of our analysis and be encouraged in other dircetions for further researc h. used SVMs for regression to predict the Canadian exchange rate, wisely recognised the problem of nonstationarity, dealt with it using experts and claimed that substantial profits were achieved. BAO, Yu-Kun, et al 2005 Forecasting Stock Composite Index by Fuzzy Support Vector Machines Regression Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Volume 6 pages 3535-3540 not cited 0 year Abstract Financial time series forecasting methods such as exponential smoothing are commonly used for prediction on stock composition index SCI and have made great contribution in practice, but efforts on looking for superior forecasting method are still made by practitioners and academia This paper deals with the application of a novel neural network technique, fuzzy support vector machines regression FSVMR , in SCI forecasting The objective of this paper is not only to examine the feasibility of FSVMR in SCI forecasting but presents our efforts on improving the accuracy of FSVMR in terms of data pre-processing, kernel function selection and parameters selection A data set from Shanghai Stock Exchange is used for the experiment to test the validity of FSVMR The experiment shows FSVMR a better method in SCI forecasting. used fuzzy support vector machines regression FSVMR to forecast a data set from the Shanghai Stock Exchange with positive results. CHEN, Kuan-Yu and Chia-Hui HO, 2005 An Improved Support Vector Regression Modeling for Taiwan Stock Exchange Market Weighted Index Forecasting ICNN s issuer credit rating systems using support vector machines Expert Systems with Applications Volume 30, Issue 3, April 2006, Pages 427-435 not cited 0 year By providing credit risk information, credit rating systems benefit most participants in financial markets, including issuers, investors, market regulators and intermediaries In this paper, we propose an automatic classification model for issuer credit ratings, a type of fundamental credit rating information, b y applying the support vector machine SVM method This is a novel classification algorithm that is famous for dealing with high dimension classifications We also use three new variables stock market information, financial support by the government, and financial support by major shareholders to enhance the effectiveness of the classification Previous research has seldom considered these variables The data period of the input variables used in this study covers three years, while most previous research has only considered one year We compare our SVM model with the back propagation neural network BP , a well-known credit rating classification method Our experiment results show that the SVM classification model performs better than the BP model The accuracy rate 84 62 is also higher than previous research. used an SVM to classify Taiwan s issuer credit ratings and found that it performed better than the back propagation neural network BP model. CHEN, Wun-Hua, Jen-Ying SHIH and Soushan WU, 20 06 Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets International Journal of Electronic Finance Volume, Issue 1, pages 49-67 not cited 0 year Abstract Recently, applying the novel data mining techniques for financial time-series forecasting has received much research attention However, most researches are for the US and European markets, with only a few for Asian markets This research applies Support-Vector Machines SVMs and Back Propagation BP neural networks for six Asian stock markets and our experimental results showed the superiority of both models, compared to the early researchespared SVMs and back propagation BP neural networks when forecasting the six major Asian stock markets Both models perform better than the benchmark AR 1 model in the deviation measurement criteria, whilst SVMs performed better than the BP model in four out of six markets. GAVRISHCHAKA, Valeriy V and Supriya BANERJEE, 2006 Support V ector Machine as an Efficient Framework for Stock Market Volatility Forecasting Computational Management Science Volume 3, Number 2 April 2006 , Pages 147-160 not cited 0 year Abstract Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified Support vector machine SVM have been proposed as a complimentary volatility model that is capable to extract information from multiscale and high-dimensional market data Presented results for SP500 index suggest that SVM can efficiently work with high-dimensional inputs to account for volatility long-memory and multiscale effects and is often superior to the main-stream volatility models SVM-based framework for volatility forecasting is expected to be important in the development of the novel strategies for volatility trading, advanced risk management systems, and other applications dealing with multi-scale and high-dimensional market data. used SVMs for forecasting stock market vola tility with positive results. HOVSEPIAN, K and P ANSELMO, 2005 Heuristic Solutions to Technical Issues Associated with Clustered Volatility Prediction using Support Vector Machines ICNN B 05 International Conference on Neural Networks and Brain, 2005, Volume 3 Pages 1656-1660 not cited 0 year Abstract We outline technological issues and our fimdings for the problem of prediction of relative volatility bursts in dynamic time-series utilizing support vector classifiers SVC The core approach used for prediction has been applied successfully to detection of relative volatility clusters In applying it to prediction, the main issue is the selection of the SVC training testing set We describe three selection schemes and experimentally compare their performances in order to propose a method for training the SVC for the prediction problem In addition to performing cross-validation experiments, we propose an improved variation to sliding window experiments utilizing the output from SVC s decision function Together with these experiments, we show that accurate and robust prediction of volatile bursts can be achieved with our approach. used SVMs for classification to predict relative volatility clusters and achieved accurate and robust results. INCE, H and T B TRAFALIS, 2004 Kernel principal component analysis and support vector machines for stock price prediction Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, Volume 3 pages 2053-2058 not cited 0 year Abstract Financial time series are complex, non-stationary and deterministically chaotic Technical indicators are used with principal component analysis PCA in order to identify the most influential inputs in the context of the forecasting model Neural networks NN and support vector regression SVR are used with different inputs Our assumption is that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship This relationshi p comes from technical analysis Comparison shows that SVR and MLP networks require different inputs The MLP networks outperform the SVR technique. found that MLP neural networks outperform support vector regression when applied to stock price prediction. KAMRUZZAMAN, Joarder, Ruhul A SARKER and Iftekhar AHMAD, 2003 SVM Based Models for Predicting Foreign Currency Exchange Rates Proceedings of the Third IEEE International Conference on Data Mining ICDM 03 Pages 557-560 not cited 0 year Abstract Support vector machine SVM has appeared as a powerful tool for forecasting forex market and demonstrated better performance over other methods, e g neural network or ARIMA based model SVM-based forecasting model necessitates the selection of appropriate kernel function and values of free parameters regularization parameter and varepsilon - insensitive loss function In this paper, we investigate the effect of different kernel functions, namely, linear, polynomial, radial basis and spline on predictio n error measured by several widely used performance metrics The effect of regularization parameter is also studied The prediction of six different foreign currency exchange rates against Australian dollar has been performed and analyzed Some interesting results are presented. investigated the effect of different kernel functions and the regularization parameter when using SVMs to predict six different foreign currency exchange rates against the Australian dollar. investigated comprehensible credit scoring models using rule extraction from SVMs. NALBANTOV, Georgi, Rob BAUER and Ida SPRINKHUIZEN-KUYPER, 2006 Equity Style Timing Using Support Vector Regressions to appear in Applied Financial Economics not cited 0 year Abstract The disappointing performance of value and small cap strategies shows that style consistency may not provide the long-term benefits often assumed in the literature In this study we examine whether the short-term variation in the U S size and value premium is predictabl e We document style-timing strategies based on technical and macro - economic predictors using a recently developed artificial intelligence tool called Support Vector Regressions SVR SVR are known for their ability to tackle the standard problem of overfitting, especially in multivariate settings Our findings indicate that both premiums are predictable under fair levels of transaction costs and various forecasting horizons. used SVMs for regression for equity style timing with positive results. ONGSRITRAKUL, P and N SOONTHORNPHISAJ, 2003 Apply decision tree and support vector regression to predict the gold price Proceedings of the International Joint Conference on Neural Networks, 2003, Volume 4 Pages 2488-2492 not cited 0 year Abstract Recently, support vector regression SVR was proposed to resolve time series prediction and regression problems In this paper, we demonstrate the use of SVR techniques for predicting the cost of gold by using factors that have an effect on gold to estimate its price We apply a decision tree algorithm for the feature selection task and then perform the regression process using forecasted indexes Our experimental results show that the combination of the decision tree and SVR leads to a better performance. applied a decision tree algorithm for feature selection and then performed support vector regression to predict the gold price, their results were positive. Van GESTEL, Tony, et al 2005 Linear and non-linear credit scoring by combining logistic regression and support vector machines, Journal of Credit Risk Vol 1, No 4, Fall 2005, Pages 31-60 not cited 0 year Abstract The Basel II capital accord encourages banks to develop internal rating models that are financially intuitive, easily interpretable and optimally predictive for default Standard linear logistic models are very easily readable but have limited model flexibility Advanced neural network and support vector machine models SVMs are less straightforward to interpret but can capture mo re complex multivariate non-linear relations A gradual approach that balances the interpretability and predictability requirements is applied here to rate banks First, a linear model is estimated it is then improved by identifying univariate non-linear ratio transformations that emphasize distressed conditions and finally SVMs are added to capture remaining multivariate non-linear relations. apply linear and non-linear credit scoring by combining logistic regression and SVMs. YANG, Haiqin, et al 2004 Outliers Treatment in Support Vector Regression for Financial Time Series Prediction Neural Information Processing 11th International Conference, ICONIP 2004, Calcutta, India, November 2004, Proceedings not cited 0 year Abstract Recently, the Support Vector Regression SVR has been applied in the financial time series prediction The financial data are usually highly noisy and contain outliers Detecting outliers and deflating their influence are important but hard problems In this paper, we pr opose a novel two-phase SVR training algorithm to detect outliers and reduce their negative impact Our experimental results on three indices Hang Seng Index, NASDAQ, and FSTE 100 index show that the proposed two-phase algorithm has improvement on the prediction. proposed a novel two-phase SVR training procedure to detect and deflate the influence of outliers The method was tested on the Hang Seng Index, NASDAQ and FSTE 100 index and results were positive However, it s not clear why the significance of outliers such as market crashes should be understated. YU, Lean, Shouyang WANG and Kin Keung LAI, 2005 Mining Stock Market Tendency Using GA-Based Support Vector Machines Internet and Network Economics First International Workshop, WINE 2005, Hong Kong, China, December 15-17, 2005, Proceedings Lecture Notes in Computer Science edited by Xiaotie Deng and Yinyu Ye, pages 336-345 not cited 0 year Abstract In this study, a hybrid intelligent data mining methodology, genetic algorithm based supp ort vector machine GASVM model, is proposed to explore stock market tendency In this hybrid data mining approach, GA is used for variable selection in order to reduce the model complexity of SVM and improve the speed of SVM, and then the SVM is used to identify stock market movement direction based on the historical data To evaluate the forecasting ability of GASVM, we compare its performance with that of conventional methods e g statistical models and time series models and neural network models The empirical results reveal that GASVM outperforms other forecasting models, implying that the proposed approach is a promising alternative to stock market tendency exploration. applied a random walk RW model, an autoregressive integrated moving average ARIMA model, an individual back-propagation neural network BPNN model, an individual SVM model and a genetic algorithm-based SVM GASVM to the task of predicting the direction of change in the daily S P500 stock price index and found that their proposed GASVM model performed the best. HARLAND, Zac, 2002 Using Support Vector Machines to Trade Aluminium on the LME Proceedings of the Ninth International Conference, Forecasting Financial Markets Advances For Exchange Rates, Interest Rates and Asset Management edited by C Dunis and M Dempster not listed Abstract This paper describes and evaluates the use of support vector regression to trade the three month Aluminium futures contract on the London Metal Exchange, over the period June 1987 to November 1999 The Support Vector Machine is a machine learning method for classification and regression and is fast replacing neural networks as the tool of choice for prediction and pattern recognition tasks, primarily due to their ability to generalise well on unseen data The algorithm is founded on ideas derived from statistical learning theory and can be understood intuitively within a geometric framework In this paper we use support vector regression to develop a number of trading submodel s that when combined, result in a final model that exhibits above-average returns on out of sample data, thus providing some evidence that the aluminium futures price is less than efficient Whether these inefficiencies will continue into the future is unknown. used an ensemble of SVMs for regression to trade the three month Aluminium futures contract on the London Metal Exchange with positive results. Van GESTEL, T et al 2005 Credit rating systems by combining linear ordinal logistic regression and fixed-size least squares support vector machines, Workshop on Machine Learning in Finance, NIPS 2005 Conference, Whistler British Columbia, Canada , Dec 9 not listed. developed credit rating systems by combining linear ordinal logistic regression and fixed-size least squares SVMs. Machine Learning How Support Vector Machines can be used in Trading. What is a Support Vector Machine. A support vector machine is a method of machine learning that attempts to take input data and classify into one of tw o categories In order for a support vector machine to be effective, it is necessary to first use a set of training input and output data to build the support vector machine model that can be used for classifying new data. A support vector machine develops this model by taking the training inputs, mapping them into multidimensional space, then using regression to find a hyperplane a hyperplane is a surface in n-dimensional space that it separates the space into two half spaces that best separates the two classes of inputs Once the support vector machine has been trained, it is able to assess new inputs with respect to the separating hyperplane and classify it into one of the two categories. A support vector machine is essentially an input output machine A user is able to put in an input, and based on the model developed through training, it will return an output The number of inputs for any given support vector machine theoretically ranges from one to infinity, however in practical terms computing power does limit how many inputs can be used If for example, N inputs are used for a particular support vector machine the integer value of N can range from one to infinity , the support vector machine must map each set of inputs into N-dimensional space and find a N-1 - dimensional hyperplane that best separates the training data. Figure 1 Support Vector Machines are input output machines. The best way to conceptualize how a support vector machine works is by considering the two dimensional case Assume we want to create a support vector machine that has two inputs and returns a single output that classifies the data point as belonging to one of two categories We can visualize this by plotting it on a 2-dimensional chart such as the chart below. Figure 2 Left Support vector machine inputs mapped to a 2D chart The red circles and blue crosses are used to denote the two classes of inputs. Figure 3 Right Support vector machine inputs mapped to a 2D chart The red circles and blue cros ses are used to denote the two classes of inputs with a black line indicating the separating hyperplane. In this example, the blue crosses indicate data points that belong to category 1 and the red circles that represent data points that belong to category 2 Each of the individual data points has unique input 1 value represented by their position on the x-axis and a unique input 2 value represented by their position on the y-axis and all of these points have been mapped to the 2-dimensional space. A support vector machine is able to classify data by creating a model of these points in 2 dimensional space The support vector machine observes the data in 2 dimensional space, and uses a regression algorithm to find a 1 dimensional hyperplane aka line that most accurately separate the data into its two categories This separating line is then used by the support vector machine to classify new data points into either category 1 or category 2.The animation below illustrates the process of traini ng a new support vector machine The algorithm will start by making a random guess finding a separating hyperplane, then iteratively improve the accuracy of the hyperplane As you can see the algorithm starts quite aggressively, but then slows down as it starts to approach the desires solution. Figure 4 An animation showing a support vector machine training The hyperplane progressively converges on the ideal geometry to separate the two classes of data. The 2-dimensional scenario above presented allows us to visualize the the process of a support vector machine, however it is only able to classify a data point using two inputs What if we want to use more inputs Thankfully, the support vector machine algorithm allows us to do the same in higher dimensions, though it does become much harder to conceptualize. Consider this, you wish to create support vector machine that takes 20 inputs and can classify any data point using these inputs into either category 1 or category 2 In order to do this, the support vector machine needs to model the data in 20 dimensional space and use a regression algorithm to find a 19 dimensional hyperplane that separates the data points into two categories This gets exceedingly difficult to visualize as it is hard for us to comprehend anything above 3-dimensions, however all that you need to know is that is works in exactly the same way as it does for the 2-dimensional case. How do Support Vector Machines Work Example Is It A Schnick. Imagine this hypothetical scenario, you are a researcher investigating a rare animal only found in the depths of the Arctic called Shnicks Given the remoteness of these animals, only a small handful have ever been found let s say around 5000 As a researcher, you are stuck with the question how can I identify a Schnick. All you have at your disposal are the research papers previously published by the handful of researchers that have seen one In these research papers, the authors describe certain characteristics about the Schnicks they found, i e height, weight, number of legs, etc But all of these characteristics vary between the research papers with no discernible pattern. How can we use this data to identify a new animal as a schnick. One possible solution to our problem is to use a support vector machine to identify the patterns in the data and create a framework that can be used to classify animals as either a schnick or not a schnick The first step is to create a set of data that can be used to train your support vector machine to identify schnicks The training data is a set of inputs and matching outputs for the support vector machine to analyze and extract a pattern from. Therefore, we must decide what inputs will be used and how many Theoretically, we can have as many inputs as we want, however this can often lead to slow training the more inputs you have the more time it takes the support vector machine to extract patterns Also, you want to choose inputs values that will tend to be relatively con sistent across all schnicks For example, height or weight of the animal would be a good example of an input because you would expect that this would be relatively consistent across all schnicks However, the average age of an animal would be a poor choice of input because you would expect the age of animals identified would all vary considerably. For this reason, the following inputs were chosen. The number of legs. The number of eyes. The length of the animal s arms. The animals average speed. The frequency of the animals mating call. With the inputs chosen, we can start to compile our training data Effective training data for a support vector machine must meet certain requirements. The data must have examples of animals that are schnicks. The data must have examples of animals that are not schnicks. In this case we have the research papers of scientist that have successfully identified a schnick and listed their properties Therefore we can read these research papers and extract the data under e ach of the inputs and allocate an output of either true or false to each of the examples The training data in this case may look similar to the table below. Table 1 Example table of schnick observations. Once we have gathered the data for all of our training inputs and outputs, we can use it to train our support vector machine During the training process, the support vector machine will create a model in seven dimensional space that can be used to sort each of the training examples into either true or false The support vector machine will continue to do this until it has a model that accurately represents the training data within the specified error tolerance Once training is complete, this model can be used to classify new data points as either true or false. Does the Support Vector Machine Actually Work. Using the Schnick scenario, I have written a script that tests how well a support vector machine can actually identify new schnicks To do this, I have used the Support Vector Machine Lea rning Tool function Library that can be downloaded from the Market. To model this scenario effectively, we need to first decide what are the actual properties of a Schnick The properties I have assumed in this case have been listed in the table below If an animal satisfies all of the criteria below, then it is a Schnick. Table 2 Summary of parameters that define a schnick. Now that we have defined our Schnick, we can use this definition to experiment with support vector machines The first step is to create a function that is able to take the seven inputs for any given animal and return the actual classification of the animal as a schnick or not This function will be used to generate training data for the support vector machine as well as assess the performance of it at the end This can be done using the function below. The next step in the process is to create a function that can generate the training inputs and outputs Inputs in this case will be generated by creating random numbers withi n a set range for each of the seven input values Then for each of the sets of random inputs generated, the isItASchnick function above will be used to generate the corresponding desired output This is done in the function below. We now have a set of training inputs and outputs, it is now time to create our support vector machines using the Support Vector Machine Learning Tool available in the Market Once a new support vector machine is created, it is necessary to pass the training inputs and outputs to it and execute the training. We now have a support vector machine that has been successfully trained in identifying Scnhicks To verify this, we can test the final support vector machine by asking it to classify new data points This is done by first generating random inputs, then using the isItASchnick function to determine whether these inputs correspond to an actual Schnick, then use the support vector machine to classify the inputs and determine whether the predicted outcome matches the actual outcome This is done in the function below. I recommend playing with the values within the above functions to see how the support vector machine performs under different conditions. Why is Support Vector Machine So Useful. The benefit of using a support vector machine to extract complex pattern from the data is that it is not necessary a prior understanding of the behavior of the data A support vector machine is able to analyze the data and extract its only insights and relationships In this way, it functions similar to a black box receiving an inputs and generating an output which can prove to be very useful in finding patterns in the data that are too complex and not obvious. One of the best features of support vector machines is that they are able to deal with errors and noise in the data very well They are often able to see the underlying pattern within the data and filter out data outliers and other complexities Consider the following scenario, in performing your research on Sc hnicks, you come across multiple research papers that describe Schnicks with massively different characteristics such as a schnick that is 200kg and is 15000mm tall. Errors like this can lead to distortions your model of what a Schnick is, which could potentially cause you to make an error when classifying new Schnick discoveries The benefit of the support vector machine is that it will develop a model that agrees with the underlying pattern opposed to a model that fits all of the training data points This is done by allowing a certain level of error in the model to enable the support vector machine to overlook any errors in the data. In the case of the Schnick support vector machine, if we allow an error tolerance of 5 , then training will only try to develop a model that agrees with 95 of the training data This can be useful because it allows training to ignore the small percentage of outliers. We can investigate this property of the support vector machine further by modifying our Schni ck script The function below has been added to introduce deliberate random errors in our training data set This function will select training points at random and replace the inputs and corresponding output with random variables. This function allows us to introduce deliberate errors into our training data Using this error filled data, we can create and train a new support vector machine and compare its performance with the original one. When the script is run, it produces the following results in the Expert Log Within a training data set with 5000 training points, we were able to introduce 500 random errors When comparing the performance of this error filled support vector machine with the original one, the performance is only reduced by 1 This is because the support vector machine is able to overlook the outliers in the data set when training and is still capable of producing an impressively accurate model of the true data This suggests that support vector machines could potentially be a more useful tool in extracting complex patterns and insights from noisy data sets. Figure 5 The resulting expert log following the running of the Schnick script in the MetaTrader 5.Demo Versions. A full version of the above code can be downloaded from Code Base, however this script can only be run in your terminal if you have purchased a full version of the Support Vector Machine Learning tool from the Market If you only have a demo version of this tool downloaded, you will be limited to using the tool via the strategy tester To allow testing of the Schnick code using the demo version of the tool, I have rewritten a copy of the script into an Expert Advisor that can be deployed using the strategy tester Both of these code versions can be downloaded by following the links below. Full Version - Using a Script that is deployed in the MetaTrader 5 terminal requires a purchased version of the Support Vector Machine Learning Tool. Demo Version - Using an Expert Advisor that is deployed in the MetaTrader 5 strategy tester requires only a demo version of the Support Vector Machine Learning Tool. How Can Support Vector Machines be used in the Market. Admittedly, the Schnick example discussed above is quite simple, however there are quite a few similarities that can be drawn between this example and using the support vector machines for technical market analysis. Technical analysis is fundamentally about using historical market data to predict future price movements In the same way within the schnick example, we were using the observations made by past scientists to predict whether a new animal is a schnick or not Further, the market is plagued with noise, errors and statistical outliers that make the use of a support vector machine an interesting concept. The basis for a significant number of technical analysis trading approaches involve the following steps. Monitoring several indicators. Identifying what conditions for each indicator correlates with a potentially successful trade. Watch each of the indicators and assess when they all or most are signalling a trade. It is possible to adopt a similar approach to use support vector machines to signal new trades in a similar way The support vector machine learning tool was developed with this in mind A full description of how to use this tool can be found in the Market, so I will only give a quick overview The process for using this tool is as follows. Figure 6 The block diagram showing the process for implementing the support vector machine tool in an Expert Advisor. Before you can use the Support Vector Machine Learning Tool, it is important to first understand how the training inputs and outputs are generated. How are Training Inputs Generated. So, the indicators you want to use as inputs have been already been initialized as well as your new support vector machine The next step is to pass the indicator handles to your new support vector machine and instruct it on how to generate the training data This is done by call ing the setIndicatorHandles function This function allows you to pass the handles of initialized indicators into the support vector machine This is done by passing and integer array containing the handles The two other inputs for this function is the offset value and the number of data points. The offset value denotes the offset between the current bar and the starting bar to be used in generating the training inputs and the number of training points denoted by N sets the size your training data The diagram below illustrates how to use these values An offset value of 4 and an N value of 6 will tell the support vector machine to only use the bars captured in the white square to generate training inputs and outputs Similarly, an offset value of 8 and an N value of 8 will tell the support vector machine to only use the bars captured in the blue square to generate training inputs and outputs. Once the setIndicatorHandles function has been called, it is possible to call the genInputs function This function will use the indicator handles to passed to generate an array of input data to be used for training. Figure 7 Candle chart illustrating the values of Offset and N. How are Training Outputs Generated. Training outputs are generated by simulating hypothetical trades based on historical price data and determining whether such a trade would have been successful or unsuccessful In order to do this, there are a few parameters that are used to instruct the support vector machine learning tool how to assess a hypothetical trade as either successful or unsuccessful. The first variable is OPTRADE The value of this can either be BUY or SELL and will correspond to either hypothetical buy or sell trades If the value of this is BUY, then when generating the outputs it will only look at the potential success of hypothetical buy trades Alternatively, if the value of this is SELL, then when generating the outputs it will only look at the potential success of hypothetical sell trades. The next values used is the Stop Loss and Take Profit for these hypothetical trades The values are set in pips and will set the stop and limit levels for each of the hypothetical trades. The final parameter is the trade duration This variable is measured in hours and will ensure that only trades that are complete within this maximum duration will be deemed successful The reason for including this variable is to avoid the support vector machine signalling trades in a slow moving sideways market. Considerations to Make When Choosing Inputs. It is important to put some thought into the input selection when implementing support vector machines in your trading Similar the Schnick example, it is important to choose an input that would be expected to have similar across difference incidences For example, you may be tempted to use a moving average as an input, however since the long term average price tends to change quite dramatically over time, a moving average in isolation may not be the best input to use This is because there won t be any significant similarity between the moving average value today and the moving average values six months ago. Assume we are trading EURUSD and using a support vector machine with a moving average input to signal buy trades Say the current price is 1 10, however it is generating training data from six months ago when the price was 0 55 When training the support vector machine, the pattern it finds may only lead to a trade being signaled when the price is around 0 55, since this is the only data it knows Therefore, your support vector machine may never signal a trade until the price drops back down to 0 55.Instead, a better input to use for the support vector machine may be a MACD or a similar oscillator because the value of the MACD is independent of the average price level and only signals relative movement I recommend you experiment with this to see what produces the best results for you. Another consideration to make when choosing inputs is ensurin g that the support vector machine has an adequate snapshot of an indicator to signal a new trade You may find in your own trading experience that a MACD is only useful when you have the past five bars to look at, as this will show a trend A single bar of the MACD may be useless in isolation unless you can tell if it is heading up or down Therefore, it may be necessary to pass the past few bars of the MACD indicator to the support vector are two possible ways you can do this. You can create a new custom indicator that uses the past five bars of the MACD indicator to calculate a trend as a single value This custom indicator can then be passed to the support vector machine as a single input, or. You can use the previous five bars of the MACD indicator in the support vector machine as five separate inputs The way to do this is to initialize five different instances of the MACD indicator Each of the indicators can be initialized with a different offset from the current bar Then the five handl es from the separate indicators can be passed to the support vector machine It should be noted, that option 2 will tend to cause longer execution times for your Expert Advisor The more inputs you have, the longer it will take to successfully train. Implementing Support Vector Machines in and Expert Advisor. I have prepared an Expert Advisor that is an example of how someone could potentially use support vector machines in their own trading a copy of this can be downloaded by following this link Hopefully the Expert Advisor will allow you to experiment a little with support vector machines I recommend you copy change modify the Expert Advisor to suit your own trading style The EA works as follows. Two new support vector machines are created using the svMachineTool library One is set up to signal new Buy trades and the other is set up to signal new Sell trades. Seven standard indicators are initialized with each of their handles stored to an integer array Note any combination of indicators c an be used as inputs, they just need to be passed to the SVM in a single integer array. The array of indicator handles is passed to the new support vector machines. Using the array of indicator handles and other parameters, historical price data is used to generate accurate inputs and outputs to be used for training the support vector machines. Once all of the inputs and outputs have been generated, both of the support vector machines are trained. The trained support vector machines are used in the EA to signal new buy and sell trades When a new buy or sell trade is signaled, the trade opens along with manual Stop Loss and Take Profit orders. The initialization and training of the support vector machine are executed within the onInit function For your reference, this segment of the svTrader EA has been included below with notes. Advanced Support Vector Machine Trading. Additional capability was built into the support vector machine learning tool for the more advanced users out there The tool allows users to pass in their own custom input data and output data as in the Schnick example This allows you to custom design your own criteria for support vector machine inputs and outputs, and manually pass in this data to train it This opens up the opportunity to use support vector machines in any aspect of your trading. It is not only possible to use support vector machines to signal new trades, but it can also be used to signal the closing of trades, money management, new advanced indicators etc However to ensure you don t receive errors, it is important to understand how these inputs and outputs are to be structured. Inputs Inputs are passed to SVM as a 1 dimensional array of double values Please note that any input you create must be passed in as a double value Boolean, integer, etc must all be converted into a double value before being passed into the support vector machine The inputs are required in the following form For example, assume we are passing in inputs with 3 inputs x 5 training points To achieve this, our double array must be 15 units long in the format. A 1 B 1 C 1 A 2 B 2 C 2 A 3 B 3 C 3 A 4 B 4 C 4 A 5 B 5 C 5.It is also necessary to pass in a value for the number of inputs In the case, NInputs 3.Outputs outputs are passed in as an array of Boolean values These boolean values are the desired output of the SVM corresponded to each of the sets of inputs passed in Following the above example, say we have 5 training points In this scenario, we will pass in a Boolean array of output values that is 5 units long. When generating your own inputs and outputs, be sure that the length of your arrays matches the values you pass in If they don t match, an error will be generated notifying you of the discrepancy For example, if we have passed in NInputs 3, and inputs is an array of length 16, an error will be thrown since, a Ninputs value of 3 will mean that the length of any input array will need to be a multiple of 3 Similarly, ensure that the number of sets of inputs and the number of outputs that you pass in are equal Again, if you have NInput s 3, length of inputs of 15 and a length of outputs of 6, another error will be thrown as you have 5 sets of inputs and 6 outputs. Try to ensure you have enough variation in your training outputs For example, if you pass in 100 training points, which means an output array of length 100, and all of the values are false with only one true, then the differentiation between the true case and the false case is not sufficient enough This will tend to lead to the SVM training very fast, but the final solution being very poor A more diverse training set will often lead to a more affective SVM. Support Vector Machine Learning Tool. This is an easy-to-use tool for implementing Support Vector Machine Learning in your Expert Advisors, Indicators and other MetaTrader 5 projects. Until now, the use of support vector machine classification has been limited only by advanced coders via external java and c dll libraries This tool has been developed using only the standard MetaTrader 5 tools and provides adv anced support vector machine functionality using a very simple interface. Please note this product is not an Expert Advisor or Indicator This is a library that allows users to implement support vector machine classification in their own Expert Advisors and Indicators. What is a Support Vector Machine. Support vector machines svm are a form of machine learning that use a supervised learning algorithm to analyze data and recognize patterns to be used for classification They are used most prominently in fields such as bioinformatics and mathematics, however this library has been specifically developed with the intention to use support vector machine learning to analyze historical price data and extract patterns that can be used to generate signals. If you want to find out more about the support vector machines mechanics and how they work, I suggest you start with the Wikipedia page The article provides good overview and further links if you are interested in looking into it further. How the Li brary Works. The basic process for any support vector machine is as follows. Gather historical market price and indicator data. Use historical data to generate a set of training inputs and outputs. Use these historical inputs and outputs to train the support vector machine. Use the trained support vector machine to analyze current market price and indicator data to signal new trades. A support vector machine is basically an input output machine The user passes input s to the machine and it produces an output of either true or false If the support vector machine has not yet been trained, it will usually give only a random output for any given input To have the support vector machine produce a useful output, it must first be trained. The training of a support vector machine is done by passing in a set of inputs with a set of corresponding desired outputs The support vector machine algorithm will then use this combined dataset to extract patterns In the case of this tool, the inputs used are ind icators inputs can be any combination of standard or custom indicators selected by the user and the outputs are either true or false corresponding to whether a new trade should be opened. Once the indicators to be used as inputs have been selected by the user along with the parameters for determining outputs, the tool will generate a set of inputs and outputs to be used for training the support vector machine Once this is done, training can be commenced Once the support vector machine has been successfully trained, it can be used to take current indicator values as inputs and signal the Expert Advisor to either make a new trade, or not. Advanced Users additional functions have been included to allow users to manually create and set your training inputs and outputs This can be used for more complex applications such as signalling when to exit a trade or for money management To do this, see details below on the setInputs and setOutputs functions. A variety of functions have been included fo r both basic and advanced users These are outlined below. Training the support vector machine can consume a significant amount of memory This option sets the maximum memory footprint you want the support vector machine to take The value given is measured in MB If a memory value of 1000 MB is set, then the training algorithm will manage its memory to keep its foot print below this level This should be considered particularly if you choose to perform back testing across multiple cores For example, if I have a quad core computer corresponding to 4 local testing agents and I have 8 GB RAM, I will generally set my memory value to about 1250 MB This will mean that when training is being executed in parallel across all local agents, only a maximum of 5000 MB 4 x 1250 MB will be used leaving 3000 MB for the operating system and other programs without causing problems. This will limit the maximum number of training cycles that will occur The reason for this is to avoid the scenario where training never stops This can occasionally happen because it is trying to achieve an impossible solution Unless you have a specific reason, I recommend you don t manually change this value. This value sets the maximum error you are willing to accept from the final support vector machine The input for this is a percentage i e 0 1 is 10 error, 0 15 is 15 error If you are finding that your training doesn t converge on a solution, I recommend you increase the acceptable error tolerance value. Example How to Use the Support Vector Machine Tool to Signal Trades. An example of Expert Advisor svmTrader has been written to show a typical use of the support vector machine learning tool You can download it for free from Code Base.