{"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fchaos-r.hatenadiary.jp%2Fentry%2F2026%2F02%2F10%2F124703\" title=\"What Is Detrending Moving-Average Cross-Correlation Analysis (DMCA)? Detecting Hidden Common Components - 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>","title":"What Is Detrending Moving-Average Cross-Correlation Analysis (DMCA)? Detecting Hidden Common Components","provider_url":"https://hatena.blog","published":"2026-02-10 12:47:03","blog_title":"Ken-Chaos\u2019s Random Notes on R","author_name":"chaos_kiyono","url":"https://chaos-r.hatenadiary.jp/entry/2026/02/10/124703","categories":["Fundamentals of Fractal Time Series Analysis","DMCA"],"provider_name":"Hatena Blog","description":"The Detrending Moving-Average Algorithm, also called Detrending Moving-Average Analysis, is abbreviated as DMA. DMA is a method used to evaluate long-range autocorrelation and self-affine properties in a single time series. In contrast, Detrending Moving-Average Cross-Correlation Analysis is a metho\u2026","author_url":"https://blog.hatena.ne.jp/chaos_kiyono/","height":"190","width":"100%","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/chaos_kiyono/20260210/20260210111824.png","version":"1.0","blog_url":"https://chaos-r.hatenadiary.jp/","type":"rich"}