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.