{"id":72,"date":"2020-06-01T16:53:58","date_gmt":"2020-06-01T16:53:58","guid":{"rendered":"http:\/\/mlhpc2.peter-zaspel.de\/?p=72"},"modified":"2020-06-15T16:54:37","modified_gmt":"2020-06-15T16:54:37","slug":"wavelets-as-features-for-time-series-ml","status":"publish","type":"post","link":"https:\/\/www.peter-zaspel.de\/?p=72","title":{"rendered":"Wavelets as Features for Time Series ML"},"content":{"rendered":"\n<p>In this project, the idea would be to familiarize oneself further with the following concepts<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>time series data<\/li><li>Wavelet analysis to generate features<\/li><li>several types of machine learning models<ul><li>kernel ridge regression<\/li><li>multilayer perceptron<\/li><li>radial basis function networks<\/li><li>transfer learning using some well-known image classifier<\/li><\/ul><\/li><\/ul>\n\n\n\n<p>Application data can range from Quantum Chemistry over Finance to Health, hence is very broad.<br>The main objective would be to start with a &#8220;black box&#8221; approach, i.e. using some existing implementation of a continuous wavelet filter bank and then to develop a deeper understanding on how the choice of some parameters in the wavelet filter bank influences the prediction quality.<\/p>\n\n\n\n<p>A first reference:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/ch.mathworks.com\/help\/wavelet\/examples\/classify-time-series-using-wavelet-analysis-and-deep-learning.html\">https:\/\/ch.mathworks.com\/help\/wavelet\/examples\/classify-time-series-using-wavelet-analysis-and-deep-learning.html<\/a><\/li><li><a href=\"http:\/\/ataspinar.com\/2018\/12\/21\/a-guide-for-using-the-wavelet-transform-in-machine-learning\/\">http:\/\/ataspinar.com\/2018\/12\/21\/a-guide-for-using-the-wavelet-transform-in-machine-learning\/<\/a><\/li><li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/1614807\">https:\/\/ieeexplore.ieee.org\/document\/1614807<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this project, the idea would be to familiarize oneself further with the following concepts time series data Wavelet analysis to generate features several types of machine learning models kernel ridge regression multilayer perceptron radial basis function networks transfer learning<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[4],"tags":[],"class_list":["post-72","post","type-post","status-publish","format-standard","hentry","category-summer-research-topics"],"_links":{"self":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/posts\/72","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=72"}],"version-history":[{"count":3,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/posts\/72\/revisions"}],"predecessor-version":[{"id":115,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=\/wp\/v2\/posts\/72\/revisions\/115"}],"wp:attachment":[{"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=72"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=72"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.peter-zaspel.de\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=72"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}