Time series Forecasting using HoltWinters （使用HoltWinters时间序列预测）.pdf

Time series Forecasting using Holt-Winters Exponential Smoothing Prajakta S. Kalekar Kanwal Rekhi School of Information Technology Under the guidance of Prof. Bernard December 6, 2004 Abstract Many industrial time series exhibit seasonal behavior, such as demand for apparel or toys. Consequently, seasonal forecasting problems are of considerable importance. This report con- centrates on the analysis of seasonal time series data using Holt-Winters exponential smoothing methods. Two models discussed here are the Multiplicative Seasonal Model and the Additive Seasonal Model. 1 Introduction Forecasting involves making projections about future performance on the basis of historical and current data. When the result of an action is of consequence, but cannot be known in advance with precision, forecasting may reduce decision risk by supplying additional information about the possible out- come. Once data have been captured for the time series to be forecasted, the analyst’s next step is to select a model for forecasting. Various statistical and graphic techniques may be useful to the analyst in the selection process. The best place to start with any time series forecasting analysis is to graph sequence plots of the time series to be forecasted. A sequence plot is a graph of the data series values, usually on the vertical axis, against time usually on the horizontal axis. The purpose of the sequence plot is to give the analyst a visual impression of the nature o