方法对比
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| DLinear：时间序列预测的分解线性模型× | 状态空间模型（卡尔曼滤波器）× | |
|---|---|---|
| 领域≠ | 深度学习 | 计量经济学 |
| 方法族≠ | Machine learning | Regression model |
| 起源年份≠ | 2023 | 1990 |
| 提出者≠ | Ailing Zeng et al. | Harvey; Durbin & Koopman (state space treatment); Kalman filter |
| 类型≠ | Decomposition-based linear forecasting model | State space time series model |
| 开创性文献≠ | Zeng, A., Chen, M., Zhang, L., & Xu, Q. (2023). Are transformers effective for time series forecasting? AAAI. link ↗ | Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. DOI ↗ |
| 别名 | Decomposition Linear, DLinear Forecaster, Linear Decomposition Model, Ayrışım Doğrusal Modeli | state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter) |
| 相关≠ | 3 | 4 |
| 摘要≠ | DLinear is a lightweight time series forecasting model introduced by Zeng et al. at AAAI 2023. It challenges the prevailing assumption that Transformer-based architectures are necessary for accurate long-horizon forecasting. The model decomposes an input sequence into trend and seasonal components using a moving average filter, then applies separate single-layer linear transformations to each component before summing their outputs to produce the final forecast. | A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases. |
| ScholarGate数据集 ↗ |
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