International Business Machines Corporation
DEMAND FORECASTING WITH LARGE COLLECTIONS OF TIME SERIES DATA

Abstract:

Demand forecasting in which sets of first order differences are determined for collections of time series data for products. The first order differences identify how values within the collections of time series data change over time. The sets of first order differences are normalized to form sets of scaled first order differences such that a same scale is present between the scaled first order differences. Bins with dynamic ranges are determined for the scaled first order differences based on a distribution of the scaled first order differences; The scaled first order differences are placed into the bins to form sets of binned values in which binned values in the sets of binned values are based on numbers of scaled first order differences in the bins. The time series data are grouped into segments based on a correlation between the sets of binned values for the time series data.