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Multivariate time series forecasting via attention-based encoder-decoder framework
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DOI：10.1016/j.neucom.2019.12.118.png)
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Abstract
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Time series forecasting is an important technique to study the behavior of temporal data and forecast future values, which is widely applied in many fields, e.g. air quality forecasting, power load forecasting, medical monitoring, and intrusion detection. In this paper, we firstly propose a novel temporal attention encoder-decoder model to deal with the multivariate time series forecasting problem. It is an end-to-end deep learning structure that integrates the traditional encode context vector and temporal attention vector for jointly temporal representation learning, which is based on bi-directional long short-term memory networks (Bi-LSTM) layers with temporal attention mechanism as the encoder network to adaptively learning long-term dependency and hidden correlation features of multivariate temporal data. Extensive experimental results on five typical multivariate time series datasets showed that our model has the best forecasting performance compared with baseline methods. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Multivariate time series
Temporal attention
Multi-step forecasting
Encoder-decoder
Deep learning
Long short-term memory networks
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