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| Model ARIMA (Autoregresivni integrirani pokretni prosjek)× | PatchTST× | TSMixer× | |
|---|---|---|---|
| Područje≠ | Ekonometrija | Duboko učenje | Duboko učenje |
| Obitelj≠ | Regression model | Machine learning | Machine learning |
| Godina nastanka≠ | 2015 | 2023 | 2023 |
| Tvorac≠ | Box & Jenkins (Box-Jenkins methodology) | Nie, Y. et al. | Si-An Chen et al. (Google) |
| Vrsta≠ | Univariate time-series model | Transformer for time series forecasting | All-MLP multivariate time-series forecasting model |
| Temeljni izvor≠ | 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 | Nie, Y., Nguyen, N. H., Sinthong, P. & Kalagnanam, J. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. ICLR. link ↗ | Chen, S.-A., Li, C.-L., Yoder, N., Arik, S. O., & Pfister, T. (2023). TSMixer: An all-MLP architecture for time series forecasting. Transactions on Machine Learning Research. link ↗ |
| Drugi nazivi≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | PatchTST — Yama Tabanlı Zaman Serisi Transformer, patch-based time series transformer, channel-independent transformer | All-MLP Time Series Mixer, Time Series Mixer, TSMixer Forecasting Model, Zaman Serisi Karıştırıcı |
| Srodne≠ | 5 | 3 | 3 |
| Sažetak≠ | 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). | 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. | TSMixer is a multivariate time-series forecasting model introduced by Si-An Chen and colleagues at Google in 2023. It challenges the prevailing dominance of Transformer-based architectures by demonstrating that a simple stack of interleaved MLP layers — alternating between mixing along the time axis and mixing across feature channels — achieves strong forecasting accuracy while remaining computationally efficient and easy to interpret architecturally. |
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