A stochastic time series generator with adaptive software architecture

Stochastic time series are preferred to historic data series of shorter duration since they contain sequences that may not be observed in a relatively short historic record. Algorithms to generate stochastic time series from historic data have already been proposed. In this paper we present an implementation of an efficient stochastic time series generation algorithm and a component based front-end software system for it. The algorithm is built as three distinct and customizable components. The component based architecture allows for seamless selection of the processing steps as well as integration of new algorithms. The system has been tested successfully on several numerical experiments using hydrologic time series data to generate lengthy (1000 years) of weekly or monthly river flows for multiple locations such that all relevant statistics of the historic series are preserved in the generated series.