手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| ARIMA（自己回帰和分移動平均）モデル× | 時系列予測のための conformal prediction× | |
|---|---|---|
| 分野 | 計量経済学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2015 | 2021 |
| 提唱者≠ | Box & Jenkins (Box-Jenkins methodology) | Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI) |
| 種類≠ | Univariate time-series model | Distribution-free prediction interval wrapper |
| 原典≠ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Angelopoulos, A. N. & Bates, S. (2023). Conformal Prediction: A Gentle Introduction. Foundations and Trends in Machine Learning, 16(4), 494-591. DOI ↗ |
| 別名≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi) |
| 関連≠ | 5 | 4 |
| 概要≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | Conformal prediction is a distribution-free wrapper that turns any point forecaster — ARIMA, a neural network, or a machine-learning model — into valid prediction intervals using only its residuals. The time-series form was popularised by Xu & Xie (2021) and the modern tutorial treatment by Angelopoulos & Bates (2023). |
| ScholarGateデータセット ↗ |
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