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: 1 2 ⁢ { KLD ⁡ ( ρ 1 ⁢ Πρ 0 ) + KLD ⁡ ( ρ 0 ⁢ Πρ 1 ) } , wherein: KLD ⁡ ( ρ 1 ⁢ Πρ 0 ) = 1 2 ⁢ σ 1 2 ⁡ [ ( μ 1 - μ 0 ) 2 + ( σ 1 2 - σ 0 2 ) ] + log ⁢ σ 0 σ 1 ; and KLD ⁡ ( ρ 0 ⁢ Πρ 1 ) = 1 2 ⁢ σ 0 2 ⁡ [ ( μ 0 - μ 1 ) 2 + ( σ 0 2 - σ 1 2 ) ] + log ⁢ σ 1 σ 0 , and wherein: σ 0 is the standard deviation of the forecasted values for a first one of the variables of the variable pair, σ 1 is the standard deviation of the distribution of forecasted values for the other variable of the variable pair, μ 0 is the average value of the forecasted values for the first one of the variables of the variable pair, and μ 1 is the average value of the forecasted values for the other variable of the variable pair.