1. A computer-implemented method of extracting time-series behavior based on time-series information, the method comprising: displaying, in a graphical user interface, source data that comprises time-series data including time-series data points comprising one or more input components that collectively represent a value from an input space and one or more output components that collectively represent a value from an output space; designating training data by user selection of at least a portion of the source data; dividing into fuzzy regions a range of possible values for each component of the time-series data within the designated training data; assigning with one or more computer processors a fuzzy membership function to each fuzzy region; generating with one or more computer processors non-conflicting fuzzy rules that are determined at least in part by the fuzzy membership functions and at least in part by how one or more output components of one or more conflicting time-series data points are clustered; generating a model of time series behavior using at least the non-conflicting fuzzy rules; and displaying, in the graphical user interface, a graphical representation of the generated model.