{"url":"https://chaos-r.hatenadiary.jp/entry/2026/02/05/182419","title":"Empirical Mode Decomposition (EMD): A Step-by-Step Visual Explanation","description":"Empirical Mode Decomposition (EMD) is a method for breaking a time-series signal into oscillatory components\u2014roughly speaking, \u201cwaves inside the data.\u201d Real-world signals (heart rate, vibration, EEG, temperature, market data, etc.) often contain several rhythms at once, and those rhythms can change \u2026","type":"rich","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/chaos_kiyono/20250310/20250310120138.gif","width":"100%","published":"2026-02-05 18:24:19","categories":["Fundamentals of Time Series Analysis"],"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fchaos-r.hatenadiary.jp%2Fentry%2F2026%2F02%2F05%2F182419\" title=\"Empirical Mode Decomposition (EMD): A Step-by-Step Visual Explanation - 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>","author_url":"https://blog.hatena.ne.jp/chaos_kiyono/","provider_name":"Hatena Blog","provider_url":"https://hatena.blog","blog_title":"Ken-Chaos\u2019s Random Notes on R","height":"190","author_name":"chaos_kiyono","version":"1.0","blog_url":"https://chaos-r.hatenadiary.jp/"}