Time forecasting model algorithms for detecting trends and seasonal components in data

This topic lists and describes time forecasting model algorithms that are suitable for detecting trends and seasonal variation in data.

Automated Box and Jenkins

An example of a time forecasting model using the Automated Box and Jenkins algorithm

BoxJenkinsFigure1.png

Automated Box and Jenkins - Last Ramp

An example of a time forecasting model using the Automated Box and Jenkins - Last Ramp algorithm

BoxJenkinsFigure2.png

 

Automatic

Note

When using the Automatic algorithm setting for a time forecasting model, BMC Helix Continuous Optimization selects the specific mathematical algorithm each time a model runs. Therefore, if you are using the Automatic algorithm setting for repeated model runs and the characteristics of the data change significantly, BMC Helix Continuous Optimization might select a different mathematical algorithm for a subsequent run. Under these circumstances, the model results appear inconsistent.

Related topics

Time-forecasting-model-algorithms-for-detecting-monotonic-trends-in-data

Time-forecasting-model-algorithms-for-detecting-trends-in-seasonal-time-series

 

Tip: For faster searching, add an asterisk to the end of your partial query. Example: cert*