基于自动聚类和自回归模型的时间序列预测

Time Series Forecasting based on Automatic Clustering and Autoregressive Model

针对股指时间序列,本文提出基于自动聚类和自回归的时间序列预测模型,将自动聚类算法与经典的时间序列模型合并.利用自动聚类算法将论域进行划分,得到相应的划分区间;再利用自回归模型确定预测数据的波动量;最后结合趋势和波动量得到最后的预测值.实验结果表明,提出的模型具有较好预测效果,在预测1992年台湾股指时间序列上,优于同类预测模型.

In this paper,a time series forecasting model based on automatic clustering and autoregressive model is proposed. The model fully capitalizes on the two key technologies,automatic clustering and autoregressive model,to deal with the stock price forecasting. The automatic clustering algorithm is applied to cluster the historical stock data into intervals of different lengths.Then,an autoregressive model is utilized to determine the fluctuation quantity of the forecasted data. Finally,the forecasted stock price is obtained by integrating trend prediction with fluctuation quantity. Stock price time series is employed to compare the forecasting accuracy between the proposed model and the existing methods. The experimental results indicate that the proposed model produces better forecasting performance.