Working with time forecasting models
Examples of forecasting models
The following examples demonstrate the utility of time forecasting models.
Example 1
You want to avoid saturation of a system resource such as memory, and you can track and predict the following metrics for the resource:
- Total available (total configured)
- Usage
- Free
Because usage metrics typically exhibit an increasing trend over time, applying a time forecasting model is particularly useful in predicting these series.
At particular points in time, you want to increase the amount of total available (total configured) memory to support increasing demand, as shown in the following figure.
However, applying a time forecasting model to the amount of a resource that is free is not as useful because a free resource metric is calculated from the other metrics. The mathematical dynamics — for example, possible non-monotonicity — of these types of metrics negatively affect the accuracy and reliability of the time forecasting model algorithms.
Example 2
Based on the internet usage between August 2007 and December 2007 (indicated in blue), you want to predict the trend for the first month of 2008. This model has been fitted with a robust linear regression, the solid green line in the following figure, which indicates that usage will continue to increase in a linear fashion in the next month. The upper and lower confidence bounds for this prediction are indicated by dashed green lines.
Example 3
A forecast of orders to be received in time is required. As shown in the following figure, the time series has a sudden surge in the demand in the month of June. In this case, fitting the model with linear regression from the beginning of the time series, as in Example 2, is not useful because it does not take into consideration the sudden surge in June. To generate an accurate prediction that reflects this increase, this model has been fitted with a robust linear regression of the data from where the algorithm automatically detected the change in data behavior.