This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.

中文翻译：

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结合k-means聚类和时间序列预测算法的基于学习的预测程序的新模型

本文旨在提出一种将预测与聚类算法相结合的时间序列预测新模型。它引入了一种通过使用 k-means 聚类算法对时间序列数据进行分组来改进预测结果的新方案。它利用聚类结果得到预测数据。通常有一些用户定义的参数会影响预测结果，因此，提出了一种基于学习的程序来估计将用于预测的参数。该参数值在算法中同时计算。与其他预测算法相比，实验结果证明了所提出模型的良好结果。它的最小均方误差为 13,007.91，平均改进率为 19.83%。