1. A computerized method for detecting one or more anomalies in time-series data, comprising: collecting time-series data from an environment to provide collected time-series data, the collected time-series data having a plurality of portions; dividing the collected time-series data into a plurality of collected data segments; fitting a plurality of local models to the respective plurality of collected data segments, the plurality local models collectively forming a global model; and determining whether there is at least one anomaly in the collected time-series data or no anomalies based on a comparison between the collected time-series data and the global model, wherein the fitting selects a type of model-fitting paradigm to be applied to the collected time-series data to generate the plurality of local models on a portion-by-portion basis.