FORECASTING USING SIMPLE EXPONENTIAL SMOOTHING METHOD

By E. Ostertagová, O. Ostertag. 2012

196 citations recorded by the source corpus.

In the paper a relatively simple yet powerful and versatile technique for forecasting time series data ‐ simple exponential smoothing is described. The simple exponential smoothing (SES) is a short-range forecasting method that assumes a reasonably stable mean in the data with no trend (consistent growth or decline). It is one of the most popular forecasting methods that uses weighted moving average of past data as the basis for a forecast. The procedure gives heaviest weight to more recent observations and smaller weight to observations in the more distant past. The accuracy of the SES method strongly depends on the optimal value of the smoothing constant a. To determine the optimal a value in the paper was used a traditional optimalization method based on the lowest mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE).

Subfields: Applied Mathematics, Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/9827236bd7ed04352372374c7d06cd6cc39993fa

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Citations in the corpus

• Healthcare Data Breaches: Insights and Implications (2020, methodology): “Unlike the Simple Moving Average method, the idea of this method is to provide the highest weights to recent data points (observations) and the lowest weights to older data points (observations) [29].”