Multiservice Architecture of Software for Stochastic Fractal Time Series Forecasting

In previous studies, automation tools for constructive-synthesizing modeling of deterministic and stochastic fractal time series were developed with the usage of single-thread and multi-agent approaches. The main goal of this study is to develop an effective system for separate scaling of genetic algorithm operations (crossover and mutation) and model evaluation operations due to significant differences in operations' complexity. The service-oriented approach to the cloud-based system design was used. Each service would be responsible for the corresponding operation and would be scaled independently. To mitigate the load, the sub-population term was introduced and used as the unit of data for the evaluation process. Additionally, the system included the aggregation phase which constructs the new population from sub-parts with the application of the selection operation.