Abstract: With the rapid development of big data and Internet of Things (IoT) technologies, the application scope of multidimensional time series data has become increasingly widespread. Faced with a large amount of complex time series data characterized by non-linearity and high-dimensional redundant features, traditional time series analysis methods struggle to effectively address the complexity of multidimensional time series with high-dimensional features, resulting in suboptimal predictive performance. To address these issues, this paper proposes the GGInformer model, which improves upon the Genetic Algorithm and Informer model while incorporating the GRU network. This model not only efficiently extracts key features from multidimensional time series but also effectively addresses long-term dependency issues. To validate the predictive capability of the model, experiments are conducted on two real datasets and three public benchmark datasets, all of which demonstrated superior performance compared to the baseline models. Specifically, compared to the Informer baseline model, the GGInformer model achieves reductions in Mean Squared Error (MSE) values of 22%, 13%, 20%, 23%, and 38% across the five datasets. The experimental results indicate that the GGInformer model can effectively address the complex feature extraction challenges of multidimensional time series data and further enhance time series prediction capabilities.

Key words: multidimensional time series, feature extraction, prediction, improved genetic algorithm