Parallellism by Partition

Enhance performance by processing time series data in parallel, using partitioning for efficient model building.

For example, a user can choose PRODUCT_ID as one partition column and can generate forecasts for different products in a model build. Although a distinct smoothing model is built for each partition, all partitions share the same model settings. For example, if setting EXSM_MODEL is set to EXSM_SIMPLE, all partition models will be simple Exponential Smoothing models. Time series from different partitions can be distributed to different processes and processed in parallel. The model for each time series is built serially.