{"width":"100%","author_url":"https://blog.hatena.ne.jp/nri-atlaxblogs/","provider_url":"https://hatena.blog","author_name":"nri-atlaxblogs","url":"https://atlaxblogs.nri.co.jp/entry/20221031","height":"190","blog_title":"atlax blogs","type":"rich","published":"2022-10-31 00:00:00","blog_url":"https://atlaxblogs.nri.co.jp/","version":"1.0","provider_name":"Hatena Blog","description":"We introduced our approach to building a practical multivariate time series forecasting model. We introduced the data set from a past Kaggle Competition and created a seasonal naive model which will be our baseline.","title":"Practical Theory for Time Series Forecasting Models 2 \uff0dA Case of the Past Kaggle Competition\uff0d","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/n/nri-atlaxblogs/20250602/20250602101645.jpg","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fatlaxblogs.nri.co.jp%2Fentry%2F20221031\" title=\"Practical Theory for Time Series Forecasting Models 2 \uff0dA Case of the Past Kaggle Competition\uff0d - atlax blogs\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","categories":["\u30c7\u30fc\u30bf\u30a2\u30ca\u30ea\u30c6\u30a3\u30af\u30b9","\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9"]}