{"version":"1.0","height":"190","url":"https://chaos-r.hatenadiary.jp/entry/2026/01/26/153032","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fchaos-r.hatenadiary.jp%2Fentry%2F2026%2F01%2F26%2F153032\" title=\" Apparent 1/f Spectra Caused by Linear Trends - 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>","categories":["Fundamentals of Time Series Analysis","Spectral Analysis in R","time series analysis","power spectrum","R"],"blog_title":"Ken-Chaos\u2019s Random Notes on R","published":"2026-01-26 15:30:32","provider_name":"Hatena Blog","provider_url":"https://hatena.blog","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/chaos_kiyono/20251228/20251228182153.png","type":"rich","author_url":"https://blog.hatena.ne.jp/chaos_kiyono/","blog_url":"https://chaos-r.hatenadiary.jp/","author_name":"chaos_kiyono","title":" Apparent 1/f Spectra Caused by Linear Trends","description":"In time-series analysis, the power spectrum is a fundamental tool for investigating the properties of a stochastic process. This interpretation is perfectly valid when spectral analysis is applied to time series generated purely by a stochastic process. However, real-world data often contain additio\u2026","width":"100%"}