A novel prediction method of complex univariate time series based on k-means clustering

Time-series prediction has been widely studied and applied in various fields. For the time series with high acquisition frequency and high noise, it is very difficult to establish a prediction model directly. Therefore, it is necessary to study how to obtain the change trend information of time series accurately, and then build a prediction model for its change trend. To obtain the change trend information of the original time series effectively and establish an accurate prediction model, this paper proposes a novel prediction method of complex univariate time series based on K-means clustering. This method first obtains the change trend information of the original time series based on the K-means clustering idea, and then, a gated recurrent unit based on the input attention mechanism is used to establish a prediction model for the obtained time-series change trend information. Extensive experiments on the electromagnetic radiation dataset we collected, the AEP_hourly dataset, and the Wind Turbine Scada dataset published online, demonstrate that our proposed K-means clustering method can effectively reduce noise interference and accurately obtain the time-series change trend information. Comparative experiments of different prediction models demonstrate that our prediction model has the best prediction accuracy, and our proposed complex univariate time-series prediction algorithm has great practical value.