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× | 局所回帰 LOESS / LOWESS× | |
|---|---|---|
| 分野≠ | 計量経済学 | 機械学習 |
| 系統≠ | Regression model | Machine learning |
| 提唱年≠ | 2015 | 1979 |
| 提唱者≠ | Box & Jenkins (Box-Jenkins methodology) | William S. Cleveland |
| 種類≠ | Univariate time-series model | Local nonparametric regression smoother |
| 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 | Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836. DOI ↗ |
| 別名≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon |
| 関連≠ | 5 | 3 |
| 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). | LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots. |
| ScholarGateデータセット ↗ |
|
|