A Neural Networks Based Method for Multivariate Time-Series Forecasting

In recent years, more and more deep neural network methods have been used in the forecasting research of multivariate time series. Comparing to the traditional methods such as autoregressive models, methods based on neural networks have achieved superior results. However, the sequence data comes from all aspects, and the data contains long-term and short-term models, which is not a small challenge for data prediction research. We builds a model based on a deep neural network, and uses a convolutional network and a recurrent network in the long-term sequence data input and short-term sequence data input of the model. Convolutional networks are mainly used to extract short-term patterns in sequence data, and recurrent networks are mainly used to extract long-term patterns in sequence data. In addition, in order to solve the problem that the output of the neural network is not sensitive to the change of the input scale, we also added an autoregressive network to the model. When constructing the input of the model, we use the corresponding period characteristics to construct the two input of the model for different datasets: long-term historical data and short-term historical data. Our model has achieved better results than traditional prediction models on four public data sets: out of 32 indicators in 8 methods, 24 optimal results have been achieved.