Time series prediction model based on autoregression weight network

We propose an autoregressive weighted network (ARWNet) time series forecast-ing model inspired by the idea of ensemble learning. The model adopts the classi-cal autoregressive analysis to optimize weak learners. Meanwhile, the combined weight optimization method is used to construct an efficient, strong learner. With these methodological foundations, the scalability of the framework is greatly enhanced by the possibility of experimenting with other learners to assist in decision-making. Machine learning can provide great utility and acceptable cost in the prediction process of electricity transformers. Over the years, many research papers on time series prediction have been reported. This work will focus on the analysis using the potential properties in the series: long-term, con-tinuity, periodicity, and delay. In our experiments, the ETT-small dataset is used to compare the prediction accuracy of ARWNet and other mainstream models. All results suggest that the proposed ARWNet model demonstrates strong gener-alization ability and high predicting accuracy with delay characteristics, which outperform current popular time-series prediction methods.