https://scholars.lib.ntu.edu.tw/handle/123456789/498266
Title: | Temporal pattern attention for multivariate time series forecasting | Authors: | Shih, Shun-Yao Sun, Fan-Keng HUNG-YI LEE |
Issue Date: | 2019 | Journal Volume: | 108 | Journal Issue: | 8-9 | Start page/Pages: | 1421-1441 | Source: | Machine Learning | Abstract: | Forecasting of multivariate time series data, for instance the prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate this task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved by recurrent neural networks (RNNs) with an attention mechanism. The typical attention mechanism reviews the information at each previous time step and selects relevant information to help generate the outputs; however, it fails to capture temporal patterns across multiple time steps. In this paper, we propose using a set of filters to extract time-invariant temporal patterns, similar to transforming time series data into its “frequency domain”. Then we propose a novel attention mechanism to select relevant time series, and use its frequency domain information for multivariate forecasting. We apply the proposed model on several real-world tasks and achieve state-of-the-art performance in almost all of cases. Our source code is available at https://github.com/gantheory/TPA-LSTM. © 2019, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/498266 | ISSN: | 0885-6125 | DOI: | 10.1007/s10994-019-05815-0 | SDG/Keyword: | Electric power generation; Forecasting; Frequency domain analysis; Metadata; Solar energy; Time series; Time series analysis; Attention mechanisms; Convolutional neural network; Electricity-consumption; Multivariate forecasting; Multivariate time series; Polyphonic music; Recurrent neural network (RNNs); State-of-the-art performance; Recurrent neural networks |
Appears in Collections: | 電機工程學系 |
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