![]() The following time series shows the closing stock price of Merck & Co. The concept of level is best understood with an example. To understand how Holt-Winters Exponential Smoothing works, one must understand the following four aspects of a time series: Level ![]() Holt-Winters Exponential Smoothing: The Holt-Winters ES modifies the Holt ES technique so that it can be used in the presence of both trend and seasonality. But Holt ES fails in the presence of seasonal variations in the time series. Holt ES can be used to forecast time series data that has a trend. Holt Exponential Smoothing: The Holt ES technique fixes one of the two shortcomings of the simple ES technique. The ES technique has two big shortcomings: It cannot be used when your data exhibits a trend and/or seasonal variations. When you use ES, you are making the crucial assumption that recent values of the time series are much more important to you than older values. By choosing a suitable weighing function, the forecaster determines which historical values should be given emphasis for calculating future values of the time series.Įxponential Smoothing: The Exponential Smoothing (ES) technique forecasts the next value using a weighted average of all previous values where the weights decay exponentially from the most recent to the oldest historical value. Averaging as a time series forecasting technique has the property of smoothing out the variation in the historical values while calculating the forecast. Common weighing functions are logarithmic, linear, quadratic, cubic and exponential. The weights are often assigned as per some weighing function. ![]() Weighted Averages: A weighted average is simply an average of n numbers where each number is given a certain weight and the denominator is the sum of those n weights. The key concepts upon which Holt-Winters Exponential Smoothing is based (Image by Author) ![]()
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