Multivariate time series prediction with multi-feature analysis

Multiple time series (MTS) have complex temporal and spatial correlations and are widely used in industry, finance, and other fields. Some current MTS prediction algorithms only extract a single time or space feature and ignore the rich features contained in the periodicity of the time series. Aiming at time series multi-feature fusion of time, space, and period, we propose a combined time series prediction model MTSD based on multi- feature analysis. We propose a time feature extraction module to extract the time features of MTS. Long Short-Term Memory (LSTM) with an attention mechanism is used to extract the time dependence of sequence data. The paper also proposes to embed dynamic periodic graphs to extract the periodicity of time series data. We propose a spatial feature extraction module to extract the spatial features of MTS. We transform multiple one-dimensional sequences into two-dimensional graphic structures through data structure transformation and extract spatial relationships from time series data using a convolutional neural network (CNN). Then, the weighted average method is used to fuse the models to obtain the final prediction results. Finally, to evaluate the proposed method's performance, we conducted many experiments on four real benchmark datasets. The experimental results show that the performance of this method is better than that of several baseline methods, and it is suitable for multivariate time series data with multivariate solid correlation.