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Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| MICN: Viacškálová izometrická konvolučná sieť pre dlhodobé časové predpovedanie× | SCINet: Vzorová konvolučná a interakčná sieť pre predikciu časových radov× | |
|---|---|---|
| Odbor | Hlboké učenie | Hlboké učenie |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2023 | 2022 |
| Tvorca≠ | Huiqiang Wang et al. | Minhao Liu et al. |
| Typ≠ | CNN-based time-series forecasting architecture | Hierarchical convolutional time-series forecasting network |
| Pôvodný zdroj≠ | Wang, H., Peng, J., Huang, F., Wang, J., Chen, J., & Xiao, Y. (2023). MICN: Multi-scale local and global context modeling for long-term series forecasting. ICLR. link ↗ | Liu, M., Zeng, A., Chen, M., Xu, Z., Lai, Q., Ma, L., & Xu, Q. (2022). SCINet: Time series modeling and forecasting with sample convolution and interaction. NeurIPS. link ↗ |
| Ďalšie názvy | Multi-scale Isometric Convolution Network, Multi-scale Local and Global Context Model, MICN Forecaster, Çok Ölçekli İzometrik Evrişim Ağı | Sample Convolution and Interaction Network, SCI-Net, Temporal Downsampling Convolution Network, Örneklem Evrişim ve Etkileşim Ağı |
| Príbuzné | 2 | 2 |
| Zhrnutie≠ | MICN (Multi-scale Isometric Convolution Network) is a convolutional neural network architecture for long-term time-series forecasting introduced by Huiqiang Wang and colleagues at ICLR 2023. Its central idea is to capture both local temporal patterns and global seasonal dependencies simultaneously through multi-scale isometric convolutions combined with a merge attention mechanism, enabling efficient and expressive modeling of complex temporal dynamics without the quadratic cost of full self-attention. | SCINet is a deep learning architecture for multi-step time-series forecasting introduced by Liu et al. at NeurIPS 2022. Its core idea is a recursive binary-tree structure of SCI-Blocks, each of which splits an input sequence into odd- and even-indexed sub-sequences, applies convolutional filters to model cross-subsequence interactions, and then merges the learned representations. This hierarchical downsampling strategy enables the network to capture temporal dependencies at multiple resolutions simultaneously. |
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