EXPONENTIAL SMOOTHING - THE STATE OF THE ART
EXPONENTIAL SMOOTHING - THE STATE OF THE ART
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DOI：
10.1002/for.3980040103
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发表时间：
1985-01-01
影响因子：
3.4
通讯作者：
GARDNER, ES
中科院分区：
文献类型：
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作者：
GARDNER, ES
本文是自20世纪50年代Brown和Holt的原始工作以来指数平滑的一个重要评论。指数平滑是基于一个务实的预测方法，这是在这篇评论中共享。其目的是为指数平滑方法的应用制定最先进的指南。本文的第一部分讨论了一类相对简单的模型，这些模型依赖于Holt-Winters程序对数据进行季节性调整。接下来，我们回顾一般指数平滑（GES），它使用时间的傅立叶函数来模拟季节性。本研究根据以下问题进行综述。这些模型的有用特性是什么？应该使用哪些参数？模型应该如何初始化？在回顾了模型构建之后，我们转向基于指数平滑的预测系统的维护问题。维护领域的主题包括使用质量控制模型来检测预测误差中的偏差，自适应参数来改善对时间序列结构变化的响应，以及两阶段预测，即我们使用误差模型或其他数据模型来改善我们的初始预测。一些主要结论：在实践中通常使用的参数范围和起始值是任意的，可能会降低精度。经验证据支持霍尔特的趋势模型，而不是布朗的。长期来看，线性趋势应该受到抑制。经验证据支持Holt-Winters方法对GES的季节性数据。用标准形式证明GES是困难的，等效的ARIMA模型更简单，更有效。误差的累积和似乎是最实用的预报监测手段。没有证据表明自适应参数可以提高预测精度。事实上，情况可能正好相反。
This paper is a critical review of exponential smoothing since the original work by Brown and Holt in the 1950s. Exponential smoothing is based on a pragmatic approach to forecasting which is shared in this review. The aim is to develop state‐of‐the‐art guidelines for application of the exponential smoothing methodology. The first part of the paper discusses the class of relatively simple models which rely on the Holt‐Winters procedure for seasonal adjustment of the data. Next, we review general exponential smoothing (GES), which uses Fourier functions of time to model seasonality. The research is reviewed according to the following questions. What are the useful properties of these models? What parameters should be used? How should the models be initialized? After the review of model‐building, we turn to problems in the maintenance of forecasting systems based on exponential smoothing. Topics in the maintenance area include the use of quality control models to detect bias in the forecast errors, adaptive parameters to improve the response to structural changes in the time series, and two‐stage forecasting, whereby we use a model of the errors or some other model of the data to improve our initial forecasts. Some of the major conclusions: the parameter ranges and starting values typically used in practice are arbitrary and may detract from accuracy. The empirical evidence favours Holt's model for trends over that of Brown. A linear trend should be damped at long horizons. The empirical evidence favours the Holt‐Winters approach to seasonal data over GES. It is difficult to justify GES in standard form–the equivalent ARIMA model is simpler and more efficient. The cumulative sum of the errors appears to be the most practical forecast monitoring device. There is no evidence that adaptive parameters improve forecast accuracy. In fact, the reverse may be true.