Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | Конформное прогнозирование для временных рядов× | PatchTST× | |
|---|---|---|---|
| Область≠ | Эконометрика | Эконометрика | Глубокое обучение |
| Семейство≠ | Regression model | Regression model | Machine learning |
| Год появления≠ | 2015 | 2021 | 2023 |
| Автор метода≠ | Box & Jenkins (Box-Jenkins methodology) | Angelopoulos & Bates (tutorial); Xu & Xie (time-series EnbPI) | Nie, Y. et al. |
| Тип≠ | Univariate time-series model | Distribution-free prediction interval wrapper | Transformer for time series forecasting |
| Основополагающий источник≠ | 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 ↗ | Nie, Y., Nguyen, N. H., Sinthong, P. & Kalagnanam, J. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. ICLR. link ↗ |
| Другие названия≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | conformal prediction, distribution-free prediction intervals, EnbPI, Konformal Tahmin (Conformal Prediction — Zaman Serisi) | PatchTST — Yama Tabanlı Zaman Serisi Transformer, patch-based time series transformer, channel-independent transformer |
| Связанные≠ | 5 | 4 | 3 |
| Сводка≠ | 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). | PatchTST is a patch-based Transformer architecture for time series forecasting, introduced by Nie and colleagues in 2023, that cuts each series into overlapping patches treated as tokens and processes channels independently. It balances computational efficiency with strong accuracy on long-horizon forecasting. |
| ScholarGateНабор данных ↗ |
|
|
|