Scalable modeling for large collections of time series

A non-transitory computer readable storage medium tangibly embodying a computer readable program code having computer readable instructions that, when executed, causes a computer device to carry out a method of improving a computational efficiency of a computing platform in processing time series data, the method comprising:

• receiving the time series data comprising a plurality of time series;
• partitioning the time series data to generate a plurality of groups of time series, wherein
• each group of time series of the plurality of groups of time series comprises respective one or more time series of the plurality of time series,
• the generating of the plurality of groups of time series comprises:
• clustering the time series data into a hierarchy of partitions of related one or more time series of the plurality of time series, the hierarchy having different partition levels;
• and
• merging two or more groups of the plurality of groups of time series based on a number of the respective one or more time series in each of the two or more groups, and
• the related one or more time series correspond to the respective one or more time series of the plurality of time series;
• determining a hardware computation capability of the computing platform by evaluating a hardware capability and a present computational load of the computing platform, wherein the hardware capability of the computing platform is selected from the group consisting of:
• a number of processors of the computing platform, an amount of memory of the computing platform, and a clock speed of the computing platform;
• selecting a partition level from the different partition levels, based on the determined hardware computation capability and a modeling accuracy associated with a group focused training of forecasting models;
• defining one or more modeling tasks based on the selected partition level, each modeling task of the one or more modeling tasks comprising a respective group of time series of the plurality of groups of time series;
• and
• executing the group focused training of the forecasting models in parallel on the computing platform, wherein each modeling task of the one or more modeling tasks corresponds to the executing of the group focused training of a respective forecasting model of the forecasting models using all the time series in the respective group of time series.

A computer-implemented method, comprising:

• receiving time series data comprising a plurality of time series;
• partitioning the time series data to generate a plurality of groups of time series, wherein
• each group of time series of the plurality of groups of time series comprises respective one or more time series of the plurality of time series,
• the generating of the plurality of groups of time series comprises;
• clustering the time series data into a hierarchy of partitions of related one or more time series of the plurality of time series, the hierarchy having different partition levels;
• and
• merging two or more groups of the plurality of groups of time series based on a number of the respective one or more time series in each of the two or more groups, and
• the related one or more time series correspond to the respective one or more time series of the plurality of time series;
• determining a hardware computation capability of a computing platform by evaluating a hardware capability and a present computational load of the computing platform, wherein the hardware capability of the computing platform is selected from the group consisting of:
• a number of processors of the computing platform, an amount of memory of the computing platform, and a clock speed of the computing platform;
• selecting a partition level from the different partition levels, based on the determined hardware computation capability and a modeling accuracy associated with a group focused training of forecasting models;
• defining one or more modeling tasks based on the selected partition level, each modeling task of the one or more modeling tasks comprising a respective group of time series of the plurality of groups of time series;
• and
• executing the group focused training of the forecasting models in parallel on the computing platform, wherein each modeling task of the one or more modeling tasks corresponds to the executing of the group focused training of a respective forecasting model of the forecasting models using all the time series in the respective group of time series.