A Fuzzy C-Means Clustering-Based Hybrid Multivariate Time Series Prediction Framework With Feature Selection

Multivariate time series prediction (MTSP) stands as a significant and challenging frontier in the data science domain, garnering considerable interest among researchers. Extreme learning machine (ELM) has emerged as a popular machine learning algorithm capable of effectively addressing MTSP challenges. However, the high-dimensional and nonlinear nature of prediction information within Big Data contexts exposes certain limitations in ELM's prediction performance. To address this issue, this article proposes a hybrid MTSP framework based on fuzzy C-means (FCM) clustering coupled with feature selection. The framework begins with a possibility distribution (PD)-based feature selection algorithm designed to evaluate information quality and describe information uncertainty via multisource information fusion. Subsequently, a robust FCM algorithm is developed, optimizing the clustering process by incorporating feature differences and neighbor information of samples while employing a multimetric hybrid strategy to determine cluster numbers. Additionally, an enhanced dual-kernel ELM (EDKELM) network is established to enhance prediction capabilities. The resulting hybrid MTSP framework with feature selection excels in autonomously discovering intrinsic feature-model connections, exhibiting superior prediction performance, and demonstrating excellent generalization ability. Experimental results using real-world datasets showcase the competitiveness of the proposed framework over existing machine learning prediction models in resolving multivariate prediction challenges.