ARMemNet
| Autor: | Chanhee Park, Hongchan Roh, Jinuk Park, Sanghyun Park |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Multivariate statistics
Series (mathematics) Artificial neural network Computer science 020207 software engineering 02 engineering and technology computer.software_genre Autoregressive model 020204 information systems 0202 electrical engineering electronic engineering information engineering Leverage (statistics) Data mining Time series Encoder computer Interpretability |
| Zdroj: | SAC |
| Popis: | Recently, several studies show the powerful capability of neural networks to capture non-linear features from time series which have multiple seasonal patterns. However, existing methods rely on convolution kernels implicitly, hence neglect to capture strong long-term patterns and lack interpretability. In this paper, we propose a memory-augmented neural network named AutoRegressive Memory Network (ARMemNet) for multivariate time series forecasting. ARMemNet utilizes memory components to explicitly encode intense long-term patterns. Furthermore, each encoder is designed to leverage inherently essential autoregressive property to represent short-term patterns. In experiments on real-world dataset, ARMemNet outperforms existing baselines and validates effectiveness of memory components for complex seasonality which is prevalent in time series datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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