方法对比
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| ARIMA（自回归积分滑动平均）模型× | TimesNet：面向时间序列的二维时变建模× | |
|---|---|---|
| 领域≠ | 计量经济学 | 深度学习 |
| 方法族≠ | Regression model | Machine learning |
| 起源年份≠ | 2015 | 2023 |
| 提出者≠ | Box & Jenkins (Box-Jenkins methodology) | Haixu Wu et al. |
| 类型≠ | Univariate time-series model | 2D convolutional time-series model |
| 开创性文献≠ | 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 | Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., & Long, M. (2023). TimesNet: Temporal 2D-variation modeling for general time series analysis. ICLR. link ↗ |
| 别名≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | Temporal 2D-Variation Network, TimesNet Model, 2D Time-Series Network, Zamansal 2B Varyasyon Ağı |
| 相关≠ | 5 | 2 |
| 摘要≠ | 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). | TimesNet is a general-purpose time-series model introduced by Wu et al. at ICLR 2023. Its central idea is that univariate or multivariate time series can be reinterpreted as collections of two-dimensional temporal maps by reshaping the 1D signal according to its dominant periodicities, detected via Fast Fourier Transform. This 1D-to-2D transformation exposes both intraperiod patterns (within one cycle) and interperiod trends (across cycles), enabling powerful 2D convolutional architectures to model temporal variation. |
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