Multi-step Time Series Forecasting Based on Informer-XGBoost-GA

Abstract

Abstract Multi-step prediction of time series has great significance in practical application. To improve the accurate prediction of multistep time series, time series prediction models have good long-time dependence and are able to analyze the correlation between information in time series. In this paper, a multi-step prediction model on the basis of Informer-XGBoost-GA was proposed, first establishing a combination of Informer and XGBoost, and then optimizing the weight of the combined model through GA. By comparing the experimental results, it can be seen that the proposed that the proposed prediction model significantly outperforms the comparison model and raise the accuracy of the prediction with advantage.