{"type":"rich","blog_url":"https://chaos-r.hatenadiary.jp/","version":"1.0","url":"https://chaos-r.hatenadiary.jp/entry/2026/01/28/234448","categories":["Fundamentals of Time Series Analysis","Spectral Analysis","R","Python"],"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fchaos-r.hatenadiary.jp%2Fentry%2F2026%2F01%2F28%2F234448\" title=\"Decomposing an AR Power Spectrum into First- and Second-Order AR Components (Part 2) - 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>","published":"2026-01-28 23:44:48","height":"190","provider_name":"Hatena Blog","author_name":"chaos_kiyono","description":"In a previous article, I explained AR (autoregressive) spectral decomposition (see the link below for details): chaos-r.hatenadiary.jp This article shows how to actually perform AR spectral decomposition in R and Python. If you are comfortable with the mathematics of AR models, power-spectrum estima\u2026","author_url":"https://blog.hatena.ne.jp/chaos_kiyono/","provider_url":"https://hatena.blog","title":"Decomposing an AR Power Spectrum into First- and Second-Order AR Components (Part 2)","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/chaos_kiyono/20260118/20260118232736.png","blog_title":"Ken-Chaos\u2019s Random Notes on R","width":"100%"}