A Novel Fusion and Feature Selection Framework for Multisource Time-Series Data Based on Information Entropy

Information technology growth brings vast time-series data. Despite richness, challenges like redundancy emphasize the need for time-series data fusion research. Rough set theory, a valuable tool for dealing with uncertainty, can identify features and reduce dimensionality, enhancing time-series data fusion. The contribution of the study lies in establishing a fusion and feature selection framework for multisource time-series data. This framework selects optimal information sources by minimizing entropy. In addition, the fusion process integrates a feature selection algorithm to eliminate redundant features, preventing a sequential increase in entropy. Crucial experiments on abundant datasets demonstrate that the proposed approach outperforms several state-of-the-art algorithms in terms of enhancing the accuracy of common classifiers. This research significantly advances the field of time-series data fusion in rough set theory, offering improved accuracy and efficiency in data processing and analysis.