The choice of an appropriate forecast technique often hinges on the amount of relevant historical data that is readily available and any obvious patterns in that data. For many important forecast problems, 10 years of monthly data (120 observations) are available and appropriate for forecasting future activity. In such cases, the full range of advanced forecast techniques can be considered. If only more restricted samples of data are available for analysis, then simpler forecast methods must be used.
If trend, cyclical, seasonal, or irregular patterns can be recognized, then forecast techniques that are capable of handling those patterns can be readily selected. For example, if the data are relatively stable, a simple exponential smoothing approach may be adequate. Other exponential smoothing models are appropriate for trending and seasonal data; the same model will not be applicable in all cases.
As the forecast horizon increases, the cyclical pattern of economic data may also become significant. In these cases, the need to relate the forecast variable to economic, market, and competitive factors increases, because simple trend projections may no longer be appropriate.
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