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| DLinear: Decomposition Linear Model für Zeitreihenprognosen× | ARIMA-Modell (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Fachgebiet≠ | Deep Learning | Ökonometrie |
| Familie≠ | Machine learning | Regression model |
| Entstehungsjahr≠ | 2023 | 2015 |
| Urheber≠ | Ailing Zeng et al. | Box & Jenkins (Box-Jenkins methodology) |
| Typ≠ | Decomposition-based linear forecasting model | Univariate time-series model |
| Wegweisende Quelle≠ | Zeng, A., Chen, M., Zhang, L., & Xu, Q. (2023). Are transformers effective for time series forecasting? AAAI. link ↗ | 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 |
| Aliasnamen≠ | Decomposition Linear, DLinear Forecaster, Linear Decomposition Model, Ayrışım Doğrusal Modeli | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Verwandt≠ | 3 | 5 |
| Zusammenfassung≠ | 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. | 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). |
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