{"categories":["Fundamentals of Fractal Time Series Analysis"],"author_url":"https://blog.hatena.ne.jp/chaos_kiyono/","height":"190","type":"rich","title":"Which Is Better for Long-Range Dependence and Fractal Time Series Analysis: DFA or DMA?","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fchaos-r.hatenadiary.jp%2Fentry%2F2026%2F02%2F20%2F161653\" title=\"Which Is Better for Long-Range Dependence and Fractal Time Series Analysis: DFA or DMA? - 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>","image_url":"https://www.researchgate.net/publication/261732358/figure/fig2/AS%3A669296355188745%401536584094229/Color-online-The-log-log-plots-of-fluctuation-functions-F-q-s-vs-scale-s-color.png","blog_url":"https://chaos-r.hatenadiary.jp/","url":"https://chaos-r.hatenadiary.jp/entry/2026/02/20/161653","description":"Detrended Fluctuation Analysis (DFA) is a widely used method for analyzing long-range correlations in time series data. It is often applied to signals related to: Long-memory processes Fractal time series Fractional Brownian motion (fBm) Fractional Gaussian noise (fGn) ARFIMA models According to the\u2026","author_name":"chaos_kiyono","width":"100%","version":"1.0","provider_url":"https://hatena.blog","provider_name":"Hatena Blog","blog_title":"Ken-Chaos\u2019s Random Notes on R","published":"2026-02-20 16:16:53"}