Temporal Pattern Attention for Multivariate Time Series Forecasting

2018-09-12 · Shun-Yao Shih, Fan-Keng Sun, Hung-Yi Lee

Forecasting multivariate time series data, such as 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 the task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism. Typical attention mechanism reviews the information at each previous time step and selects the relevant information to help generate the outputs, but it fails to capture the temporal patterns across multiple time steps. In this paper, we propose to use a set of filters to extract time-invariant temporal patterns, which is similar to transforming time series data into its "frequency domain". Then we proposed a novel attention mechanism to select relevant time series, and use its "frequency domain" information for forecasting. We applied the proposed model on several real-world tasks and achieved state-of-the-art performance in all of them with only one exception.

📄 PDF Abstract BibTeX arXiv:1809.04206

Code (4)

gantheory/TPA-LSTM 공식 구현 tf

abinashsinha330/Air-Pollution-Forecasting-using-Machine-Learning-APF-Model tf

abinashsinha330/Air-Pollution-Forecasting-using-Recurrent-Neural-Networks tf

abinashsinha330/Solve-Air-Air-Pollution-Forecasting-using-Deep-Attentive-Sequence-Learning tf

Tasks

Multivariate Time Series ForecastingTime SeriesTime Series Analysis Time Series Forecasting Univariate Time Series Forecasting

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