Abstract
Deep learning models are playing an increasingly important role in time series forecasting with their excellent predictive ability and the convenience of not requiring complex feature engineering. However, the existing deep learning models still have shortcomings in dealing with periodic and long-distance dependent sequences, which lead to unsatisfactory forecasting performance on this type of dataset. To handle these two issues better, this paper proposes a novel periodic time series forecasting model based on DA-RNN, called DA-SKIP. Using the idea of task decomposition, the novel model, based on DA-RNN, GRU-SKIP and autoregressive component, breaks down the prediction of periodic time series into three parts: linear forecasting, nonlinear forecasting and periodic forecasting. The results of the experiments on Solar Energy, Electricity Consumption and Air Quality datasets show that the proposed model outperforms the three comparison models in capturing periodicity and long-distance dependence features of sequences.
Highlights
Time series forecasting can be summarized as a process of extracting useful information from historical records and forecasting the future value [1]
To solve the long-distance dependence problem and sequence periodicity problem in time series forecasting better, this paper introduces the periodic gated recurrent network component (GRU-SKIP) and autoregressive component into the DA-RNN model to construct a new model called DA-SKIP that is more suitable for periodic time series datasets
The result result of of the the experiment series prediction accuracy of the model is significantly improved compared with the existing model, and DA-SKIP model is significantly improved compared with the existing model, and the de- the degree improvement related characteristics of the dataset itself
Summary
Time series forecasting can be summarized as a process of extracting useful information from historical records and forecasting the future value [1]. It has shown great application value in stock trend forecasting [2], traffic flow forecasting [3], power generation [4], electricity consumption forecasting [5], tourism passenger flow forecasting [6], weather forecasting [7] and other fields. Among the problems in time series forecasting, the biggest problem faced by existing models is capturing the long-distance dependence in sequences. If the periodicity of time series is taken into consideration in optimizing the model, the applicability of the model can be improved such that it can achieve better performance on this type of dataset
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