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| Koopa: 非定常時系列のためのクープマン予測器× | DLinear: 時系列予測のための分解線形モデル× | |
|---|---|---|
| 分野 | 深層学習 | 深層学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年 | 2023 | 2023 |
| 提唱者≠ | Yong Liu et al. | Ailing Zeng et al. |
| 種類≠ | Koopman operator-based time-series forecasting model | Decomposition-based linear forecasting model |
| 原典≠ | Liu, Y., Li, C., Wang, J., & Long, M. (2023). Koopa: Learning non-stationary time series dynamics with Koopman predictors. NeurIPS. link ↗ | Zeng, A., Chen, M., Zhang, L., & Xu, Q. (2023). Are transformers effective for time series forecasting? AAAI. link ↗ |
| 別名 | Koopman Predictor, Koopman-based Time-Series Model, Koopa Forecaster, Koopman Tahmincisi | Decomposition Linear, DLinear Forecaster, Linear Decomposition Model, Ayrışım Doğrusal Modeli |
| 関連 | 3 | 3 |
| 概要≠ | Koopa is a deep learning model for time-series forecasting introduced by Yong Liu, Chang Li, Jianmin Wang, and Mingsheng Long at NeurIPS 2023. It addresses the challenge of non-stationarity by disentangling time series into stationary and non-stationary components, then modeling the non-stationary dynamics using a learned approximation of the Koopman operator — a mathematical framework that lifts nonlinear systems into a linear space for tractable long-horizon prediction. | 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. |
| ScholarGateデータセット ↗ |
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