
Part 2. MM4XL Tools > 1. Strategic Tools > Forecast Manager > 3. Technicalities > Forecasting Technique Selection Forecast ManagerForecasting Technique SelectionThe selection of the appropriate forecasting technique depends on several critical factors, some of which are more common then others. In our experience, 3 factors are worth considering in this help manual: forecast horizon, data pattern, and level of accuracy. Forecast horizonThis is the number of periods the forecast should go into the future after the last known value. Typically, decisionmakers are interested in one of the following:
The longterm forecast tends to relate to trend factors (e.g., product demand, market size, industry structure, etc.). The shortterm is tied to seasonality and cyclical variations. In general, longterm horizon forecasts find causal methods (regression) more valuable and autoregressive methods become less valuable. In the shortterm, however, when dealing with stable series (that exhibit few turningpoints) autoregressive methods may become very powerful. Level of accuracyIt is dependent on the project for which the forecast has to be made. In some cases, a rough approximation of the trend pattern may be enough to the end user. When a high level of accuracy is required, Forecast Manager makes available all indices and charts needed for judging whether the model is accurate enough or a better one can be devised among the 14 available models. Tip: The accuracy of forecast can be inspected visually or tested by means of statistical measures. In both cases the process is based on the analysis of the error associated with each forecasted items, and its ultimate goal is to test how well one model forecasts (fits the actual curve). The forecast error is computed with the formula:
The smaller the error term_{} the better the forecast. _{} represents the actual sales level and _{}the forecasted one. However, one model can forecast better under certain circumstances and less well under others. Therefore, it is highly recommended to test the quality of forecast by means of several measures and to compare forecasts obtained with different models, as we shall see.Graphical methods inspect the reliability of forecasts quickly and accurately, and they help to identify systematic error patterns produced by the model.
The pictures above show how the same actual data were fitted with different models. Sometimes one cannot recognize by simple visual inspection, which model is best, so plotting error terms helps in discerning among the various methods. When the model forecasts accurately the error terms are randomly scattered around the zero errorline. There are three most common ways for inspecting forecast reliability by means of graphical methods:
Data patternTrend and seasonality are the two core elements of any shortterm forecasting exercise made by means of autoregressive models. Both elements can be found or not in a time series. When they are present, also separately, they take either an additive or a multiplicative form. Additive trend or seasonality is one that increases over time at a regular rate (panel A and B in the picture below). On the other side, a multiplicative trend or seasonality increases at a faster rate than in the past (panel C and D).
In general, regression models can replicate any pattern, given the forecast manager can identify, measure, and gather the relevant independent variables that explain the model. When forecasting shortterm, the autoregressive and regression models available in Forecast Manager offer a valuable solution to fit any curve form resembling the curves shown above. However, unstable series, series that show a high number of turningpoints, may produce weak results. 