Forecasting trends in time series. (English) Zbl 0617.62105

Most time series methods assume that any trend will continue unabated, regardless of the forecast leadtime. But recent empirical findings suggest that forecast accuracy can be improved by either damping or ignoring altogether trends which have a low probability of persistence.
This paper develops an exponential smoothing model designed to damp erratic trends. The model is tested using the sample of 1,001 time series first analyzed by S. Makridakis et al. [The accuracy of extrapolation (time series) methods: Results of a forecasting competition. J. Forecasting 1, 111-153 (1982)]. Compared to smoothing models based on a linear trend, the model improves forecast accuracy, particularly at long leadtimes. The model also compares favorably to sophisticated time series models noted for good long-range performance, such as those of R. Lewandowski [Sales forecasting by FORSYS. ibid. 1, 205-214 (1982)] and E. Parzen [ARARMA models for time series analysis and forecasting. ibid. 1, 67-82 (1982)].


62M20 Inference from stochastic processes and prediction
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
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