{"blog_title":"Ken-Chaos\u2019s Random Notes on R","author_name":"chaos_kiyono","width":"100%","description":"In this article, I explain power spectrum estimation (spectral analysis) for discrete time series\u2014the kind of data we work with in practice. In many time-series textbooks, analysis is developed under the assumption of a weakly stationary process. In that setting, the power spectrum plays a central r\u2026","provider_url":"https://hatena.blog","blog_url":"https://chaos-r.hatenadiary.jp/","categories":["Fundamentals of Time Series Analysis","stochastic process","power spectrum"],"version":"1.0","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/chaos_kiyono/20250113/20250113175518.png","provider_name":"Hatena Blog","type":"rich","title":"Power Spectrum Estimation for Discrete Time Series: Real Data Are Neither Continuous nor Infinitely Long","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fchaos-r.hatenadiary.jp%2Fentry%2F2026%2F01%2F28%2F161928\" title=\"Power Spectrum Estimation for Discrete Time Series: Real Data Are Neither Continuous nor Infinitely Long - 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 16:19:28","url":"https://chaos-r.hatenadiary.jp/entry/2026/01/28/161928","height":"190","author_url":"https://blog.hatena.ne.jp/chaos_kiyono/"}