{"image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/chaos_kiyono/20251217/20251217174215.png","type":"rich","categories":["Fundamentals of Time Series Analysis","Spectral Analysis in R","power spectrum","R","stochastic process"],"author_name":"chaos_kiyono","height":"190","version":"1.0","width":"100%","blog_title":"Ken-Chaos\u2019s Random Notes on R","provider_name":"Hatena Blog","author_url":"https://blog.hatena.ne.jp/chaos_kiyono/","url":"https://chaos-r.hatenadiary.jp/entry/2026/01/26/004007","blog_url":"https://chaos-r.hatenadiary.jp/","provider_url":"https://hatena.blog","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fchaos-r.hatenadiary.jp%2Fentry%2F2026%2F01%2F26%2F004007\" title=\" Practical Power Spectral Density Estimation: Uncovering the Hidden True Structure - 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":"2026-01-26 00:40:07","title":" Practical Power Spectral Density Estimation: Uncovering the Hidden True Structure","description":"Estimating the power spectrum of a time series is one of the most fundamental analysis methods for detecting periodic components and for investigating the basic properties of the underlying stochastic process. This class of methods is collectively known as spectral analysis. In this article, we expl\u2026"}