Time series research in academic and industrial fields has attracted wide attention. However, the frequency information contained in time series still lacks effective modeling. The studies found that time series forecasting relies on different frequency patterns: short-term series forecasting relies more on high-frequency components, while long-term forecasting focuses more on low-frequency data. To better describe the multifrequency mode, a dual-stage multifeature adaptive frequency domain prediction model (DMAFD) is proposed in this paper. DMAFD contains two stages. First, it adopts the XGBoost algorithm to obtain a feature vector by analyzing the feature importance. Second, the frequency feature extraction of time series and the frequency aware modeling of the target sequence is integrated, for building an end-to-end prediction network based on the dependence of time series on frequency mode. The innovation is reflected in the fact that the prediction network can automatically focus on multifrequency components according to the dynamic evolution of the input sequence. Extensive experiments on four real data sets from different fields show that DMAFD obtains higher accuracy and smaller lags in time step analysis compared with state-of-the-art algorithms.

Multiview spectral clustering aims to separate data into different clusters efficiently by the use of multiview information. Many studies learn the affinity matrix from the original high-dimensional data, whose noise goes against the clustering results. Besides, some methods based on self-representation subspace clustering have a high time complexity. In this paper, we propose a simple, yet effective, and efficient method named Kernel-based Low-rank Tensorized Multiview Spectral Clustering (KLTMSC) to address these issues. Instead of using the original data to get the affinity matrix, KLTMSC learns the affinity matrix from kernel representation of the high-dimensional data to reduce the noisy information. Furthermore, to be robust to noise, the low-rank tensor is learned in the process of exploring the high-order correlations between data. Experiments on real-world data sets show that our method not only yields better results but also is quite time-saving compared with other state-of-the-art models.