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中文摘要：
将最优梯度算法应用于指数平滑模型,通过构造优选并自动生成的最佳平滑参数使模型得以优化,增强了指数平滑模型对时间序列的适应能力,较好地解决了指数平滑预测中,平滑参数靠检验确定且为静态、平滑初值难以确定并导致预测偏差等问题。通过实例,与其他指数平滑预测算法相比,验证了该算法的有效性。
英文摘要：
This paper proposed a new class of exponential smoothing model with dynamic smoothing parameter without selecting the initial smoothing parameter. Constructed the algorithm to select an optimal parameter for optimizing the new model, which enables the model to better adapt to time series so that some problems, e. g. , the parameter was static and determined only by one＇ s experiences, and smoothing initial value was hard to determine which may easily lead to a prediction deviation, are better resolved. Finally, using real traffic data, this paper compared this model with other forecast methods to validate the efficiency of this algorithmic.
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