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| Model ARIMA (Autoregresif Bersepadu Purata Bergerak)× | Informer× | TimesNet: Pemodelan Variasi Temporal 2D untuk Siri Masa× | |
|---|---|---|---|
| Bidang≠ | Ekonometrik | Pembelajaran Mendalam | Pembelajaran Mendalam |
| Keluarga≠ | Regression model | Machine learning | Machine learning |
| Tahun asal≠ | 2015 | 2021 | 2023 |
| Pengasas≠ | Box & Jenkins (Box-Jenkins methodology) | Zhou, H. et al. | Haixu Wu et al. |
| Jenis≠ | Univariate time-series model | Transformer (ProbSparse self-attention) | 2D convolutional time-series model |
| Sumber perintis≠ | 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 | Zhou, H. et al. (2021). Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. AAAI. DOI ↗ | 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 ↗ |
| Alias≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | Informer — Uzun Dizi Transformer Tahmini, Informer transformer, ProbSparse attention forecaster | Temporal 2D-Variation Network, TimesNet Model, 2D Time-Series Network, Zamansal 2B Varyasyon Ağı |
| Berkaitan≠ | 5 | 5 | 2 |
| Ringkasan≠ | 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). | Informer is a Transformer-based model introduced by Zhou et al. in 2021 for long-sequence time-series forecasting, using a ProbSparse self-attention mechanism that lowers the computational complexity of the standard Transformer to O(L log L). It is built for problems that demand predictions across thousands of future steps. | 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. |
| ScholarGateSet data ↗ |
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