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This documentation supports releases of BMC Helix Continuous Optimization up to December 31, 2021. To view the latest version, select the version from the Product version menu.

Time forecasting model algorithms for detecting trends in seasonal time series

This topic lists and describes time forecasting model algorithms that are suitable for detecting trends in monotonic data:

Linear - By Time Shifts

An example of a time forecasting model using the Linear - By Time Shifts algorithm

PR_RUN_4821_21-BY-TIME-SHIFTS-2.png

Holt-Winters

An example of a time forecasting model using the Holt-Winters algorithm

PR_RUN_3981_6-HOLT-WINTERS-3.png

Linear - Yearly Time Shift

An example of a time forecasting model using the Linear - Yearly Time Shift algorithm

PR_RUN_30068_2-YEARLY.png

Related topics

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

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

 

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BMC Helix Continuous Optimization 21.3