Forecasting With Adaptive Gradient Exponential Smoothing

Forecasting With Adaptive Gradient Exponential Smoothing By A. FEUER* (Manuscript received December 20, 1982) Exponential Smoothing (ES) as a forecasting technique has been extensively used since its introduction in the 1960s. It is simple, hence easy to implement, and in many cases performs surprisingly well. However, many phenomena require a more sophisticated forecasting technique. In this paper we introduce a new forecasting technique, Adaptive Gradient Exponential Smoothing (AGES). This technique extends the classical ES as used on simple data or on data with linear trend. For data with both linear trend and seasonal effects this extension results in a new and more general form of ES, which is presented in this paper. The new forecasting technique is tested on simulated data and some real data of the types mentioned above, and its performance in all t h e s e t e s t s is clearly superior to E S . It is shown by analysis a n d by t h e experimentations that for certain types of data it does in fact converge to the optimal (in the mean square error sense) forecasts. I. INTRODUCTION The need for quick and reliable forecasts of various time series is often encountered in economic and business situations. In the Bell System, forecasting is used to help plan trunk and facilities for the telephone network, 1 " 3 as well as to project computer workload, to determine staffing levels for operators or service observers, and more. Many forecasting techniques exist and different time series may require different techniques.