ARMemNet: autoregressive memory networks for multivariate time series forecasting
Pages 1094 - 1097
Abstract
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.
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Index Terms
- ARMemNet: autoregressive memory networks for multivariate time series forecasting
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Published In
March 2021
2075 pages
ISBN:9781450381048
DOI:10.1145/3412841
- Conference Chairs:
- Chih-Cheng Hung,
- Jiman Hong,
- Program Chairs:
- Alessio Bechini,
- Eunjee Song
Copyright © 2021 Owner/Author.
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Published: 22 April 2021
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SAC '21
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SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing
March 22 - 26, 2021
Virtual Event, Republic of Korea
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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%
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