{"published":"2026-01-28 23:23:58","provider_name":"Hatena Blog","url":"https://chaos-r.hatenadiary.jp/entry/2026/01/28/232358","height":"190","author_url":"https://blog.hatena.ne.jp/chaos_kiyono/","width":"100%","author_name":"chaos_kiyono","categories":["Fundamentals of Time Series Analysis","Spectral Analysis","time series analysis"],"blog_url":"https://chaos-r.hatenadiary.jp/","description":"In this article, we explain how an autoregressive (AR) model can be fitted to an observed time series (left panel in the figure below), and how the resulting power spectrum can be decomposed into a set of more fundamental spectral components (right panel). We refer to this procedure as AR spectral d\u2026","version":"1.0","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fchaos-r.hatenadiary.jp%2Fentry%2F2026%2F01%2F28%2F232358\" title=\"Decomposing an AR Power Spectrum into First- and Second-Order AR Components (Part 1) - 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://cdn-ak.f.st-hatena.com/images/fotolife/c/chaos_kiyono/20250131/20250131224644.png","title":"Decomposing an AR Power Spectrum into First- and Second-Order AR Components (Part 1)","type":"rich","blog_title":"Ken-Chaos\u2019s Random Notes on R","provider_url":"https://hatena.blog"}