1. A computer-implemented method of extracting time-series behavior based on time-series information, the method comprising: loading into a computer time-series data, wherein the loaded time-series data comprises time-series data points comprising an input-component-part and an output-component-part, the input-component-part comprising one or more input components and the output-component-part comprising one or more output components, the one or more input components collectively representing a value from an input space and the one or more output components collectively representing a value from an output space, wherein loading time-series data further comprises incorporating at least one time-related input component of at least one data point into the loaded time-series data; dividing into fuzzy regions a range of possible values for each component of the time-series 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 a plurality of the time-series data points are clustered; determining a mapping from the input space to the output space based on defuzzification of the fuzzy rules; and displaying the mapping in a manner that allows a user to perform time-series prediction or time-series-trend recognition.