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Systems and methods for clustering time series data based on forecast distributions

  • US 9,336,493 B2
  • Filed: 06/06/2011
  • Issued: 05/10/2016
  • Est. Priority Date: 06/06/2011
  • Status: Active Grant
First Claim
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1. A computer-implemented method, comprising:

  • accessing a set of variables, wherein accessing is performed at a computing device;

    accessing time series data with respect to each of the variables of the set;

    with respect to each of the variables of the set, forecasting values and determining a distribution of the forecasted values, wherein forecasting includes using a forecasting model and the time series data accessed with respect to each of the variables;

    identifying all possible variable pairs, wherein each of the variable pairs consists of two of the variables of the set;

    with respect to each of the variables pairs, calculating a divergence metric using the distributions of the forecasted values for the variables of the respective variable pair; and

    defining clusters such that at least one of the clusters includes two or more of the variables of the set, wherein defining the clusters is performed using a hierarchical clustering algorithm and is based on the calculated divergence metrics, wherein each of the divergence metrics is a symmetric Kullback-Leibler divergence metric, wherein, with respect to each of the variable pairs, the calculated symmetric Kullback-Leibler divergence metric is equal to;

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