1. A method for mode determination for multivariate time series data, comprising: receiving at least one multivariate time series comprising historical data; determining a first number of modes for the at least one multivariate time series, each mode comprising one or more time periods in the multivariate time series; for each mode of the first number of modes, determining a first respective set of within-mode parameters that describe behavior of the at least one multivariate time series during the one or more time periods corresponding to the mode; determining a first set of cross-mode parameters for the first number of modes that describe behavior of the at least one multivariate time series across the first number of modes; determining a first likelihood of the at least one multivariate time series based on the first sets of within-mode parameters and first set of cross-mode parameters; determining a second number of modes for the at least one multivariate time series; for each mode of the second number of modes, determining a second respective set of within-mode parameters that describe behavior of the at least one multivariate time series during the one or more time periods corresponding to the mode; determining a second set of cross-mode parameters for the second number of modes that describe behavior of the at least one multivariate time series across the second number of modes; determining a second likelihood of the at least one multivariate time series based on the second sets of within-mode parameters and second set of cross-mode parameters; and based on the first likelihood being higher than the second likelihood, selecting the first number of modes to model the at least one multivariate time series.