Forecast Fit – Residual Analysis
Residuals indicate the difference between your chosen forecasting method and actuals. You can look at residuals over time and their distribution to understand how well the chosen forecast method fits to your historic data.
A residual time graph shows the difference between forecasts (red line) and actuals (blue line).
Reading a Residual Time Graph
The residual time graph illustrates the difference between forecast values and your actual historic data over time.
Ideally, a residual graph will look like noise. It should contain no discernible patterns or repeated column groupings. Otherwise, the graph indicates that the forecasting method is not picking up on seasonality, recurring promotions, price changes, or other events. You may also be missing exponential growth or decay.
Reading a Residual Distribution Graph
The peak of this graph should always be centered at or around zero. If it is not peaked near zero, it indicates that your forecasting method is biased, meaning it is systematically over- or under-forecasting. For example, your data might contain a growing or declining trend but is being modeled with a level method. Alternatively, your data might contain exponential growth but is being modeled with a linear trend method.
If your graph does not have a bell shape (e.g. hill shape), it most likely means that you have outliers that are skewing your bell curve to have a longer tale on one side versus the other. In some cases, you might just have too little data to create a smooth distribution graph; in this case you should ignore the graph until you have a more representative sample of residuals.