Abstract: The seasonality of seasonal time series is reconstructed to transform the multi-step ahead forecasting into a single-step forecasting. According to the characteristics of every single-step forecasting time series, a forecasting selection approach is presented. As for every single-step forecasting, most proper forecasting method comes up, then the method selected is used to build a model to predict seasonality. Combining the forecasted trend with the predicted values obtained by a grey forecasting model, the integral seasonal time series forecasting model is established. The comparison of forecasting results show that this model outperforms the multi-step ahead forecasting with better forecasting performance.

Key words: seasonality reconstruction, method selection, seasonality forecasting, seasonal time series

摘要： 根据每个单步预测序列各自具有的特征，通过周期项重构把多步预测转化为单步预测，提出一种预测方法选择策略。为每个单步预测序列选择一个最合适的预测方法，利用选择的方法建模预测周期项，结合灰色预测模型对趋势项的预测值，建立季节性时间序列整体预测模型。实验结果表明，该模型能克服周期项多步预测的缺点，具有较高的预测精度。

关键词: 周期项重构, 方法选择, 周期项预测, 季节性时间序列

CLC Number: 

• TP18