On combining forecasts: some extensions and results

Article Abstract:

Combining forecasts may improve the end model's predictive accuracy sufficiently to justify their use over a more expensive single model. Three separate models, each predicting gasoline consumption, can be combined in a number of different ways, with varying levels of accuracy, to accommodate different end-user requirements. The development of such combined forecasts, by compiling the data included in individual forecasts, is discussed form the viewpoints of motivation, evaluation and methodology. Research supporting combined forecasts as an economical method for identifying optimal forecasted data is also reviewed.

Statistics, Statistics (Data)

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A simple method of computing prediction intervals for time series forecasts

Article Abstract:

Almost all point forecasts are wrong, and prediction intervals may be necessary to help ascertain the likely precision of management forecasting. An empirical approach to prediction intervals assumes very little. Any error variances can be computed at different forecast lead times during model-fitting. Then the Chebyshev inequality can be applied to determine prediction intervals.

Inventory control

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Forecasting trends in time series

Article Abstract:

Forecast accuracy can be improved by damping or eliminating low probability trends. An exponential smoothing model, if employed to suppress such trends, significantly improves forecasting, being much improved over that obtained using linear trend smoothing models. This is especially true for trends with long lead times.

Research, Innovations, Mathematical models, Time-series analysis, Time series analysis

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