时间序列指数平滑模型新体系及算法

New System and Algorithm of Exponential Smoothing Models of Time Series

  • 摘要: 本文提出了动态平滑参数的概念,由此建立了一类不需选取平滑初值、带有动态平滑参数的指数平滑新模型体系,包括差分-指数平滑模型;又以预测误差平方和SSE最小为目标,构造了优选并自动生成最佳平滑参数使平滑模型得以优化的最速下降算法,增强了指数平滑模型对时间序列的适应能力.从而较完整的解决了指数平滑预测中,平滑参数靠经验确定且为静态、平滑初值难以确定并易导致预测偏差等问题.

     

    Abstract: A new class of exponential smoothing models with the dynamic parameter, including the difference-exponential models, are put forward, it is unnecessary to put any initial value in time series smoothing. Aiming the square sum of error (SSE), we construct the algorithm to iterate and select an optimal parameter for optimizing the new models, which adapts the model to time series more. So some questions, i.e., the parameter is static and determined only by one’s experiences, and smoothing initial value isn’t easy to determine and leads to a deviation easily, are resolved completely.

     

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