{"author_name":"chaos_kiyono","categories":["C Program","R","fractal","long-range correlation","time series analysis","stochastic process"],"author_url":"https://blog.hatena.ne.jp/chaos_kiyono/","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/chaos_kiyono/20260118/20260118174323.png","title":"Fast DMA in C: Beating the Performance Bottleneck of Convolution-Based DMA for Long Data Sets","description":"In this post, we\u2019re going to put our fast DMA (Detrending Moving Average Analysis) algorithm to the test in C to see just how quick it really is. If you code DMA the \"obvious\" way\u2014by directly calculating the Savitzky\u2013Golay filter convolution\u2014the computation becomes painfully slow for long time serie\u2026","height":"190","width":"100%","provider_name":"Hatena Blog","version":"1.0","blog_url":"https://chaos-r.hatenadiary.jp/","provider_url":"https://hatena.blog","published":"2026-01-19 00:08:56","blog_title":"Ken-Chaos\u2019s Random Notes on R","url":"https://chaos-r.hatenadiary.jp/entry/2026/01/19/000856","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fchaos-r.hatenadiary.jp%2Fentry%2F2026%2F01%2F19%2F000856\" title=\"Fast DMA in C: Beating the Performance Bottleneck of Convolution-Based DMA for Long Data Sets - Ken-Chaos\u2019s Random Notes on R\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","type":"rich"}