{"id":10450,"date":"2013-09-04T00:04:44","date_gmt":"2013-09-03T21:04:44","guid":{"rendered":"http:\/\/hgpu.org\/?p=10450"},"modified":"2013-09-04T00:04:44","modified_gmt":"2013-09-03T21:04:44","slug":"detecting-multiple-periodicities-in-observational-data-with-the-multi-frequency-periodogram-ii-frequency-decomposer-a-parallelized-time-series-analysis-algorithm","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10450","title":{"rendered":"Detecting multiple periodicities in observational data with the multi-frequency periodogram. II. Frequency Decomposer, a parallelized time-series analysis algorithm"},"content":{"rendered":"<p>This is a parallelized algorithm performing a decomposition of a noisy time series into a number of frequency components. The algorithm analyses all suspicious periodicities that can be revealed, including the ones that look like an alias or noise at a glance, but later may prove to be a real variation. After selection of the initial candidates, the algorithm performs a complete pass through all their possible combinations and computes the rigorous multi-frequency statistical significance for each such frequency tuple. The largest combinations that still survived this thresholding procedure represent the outcome of the analysis. The parallel computing on a graphics processing unit (GPU) is implemented through CUDA and brings a significant performance increase. It is still possible to run FREDEC solely on CPU in the traditional single-threaded mode, when no suitable GPU device is available.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This is a parallelized algorithm performing a decomposition of a noisy time series into a number of frequency components. The algorithm analyses all suspicious periodicities that can be revealed, including the ones that look like an alias or noise at a glance, but later may prove to be a real variation. After selection of the [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,96,89,3],"tags":[1787,1794,14,97,20,1186,176],"class_list":["post-10450","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-astrophysics","category-nvidia-cuda","category-paper","tag-algorithms","tag-astrophysics","tag-cuda","tag-instrumentation-and-methods-for-astrophysics","tag-nvidia","tag-nvidia-geforce-gt-210","tag-package"],"views":2799,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10450","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=10450"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10450\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10450"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10450"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10450"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}