Agent-based distributed time series forecasting system

Many studies have demonstrated that agent-based distributed computing improves quality of distributed computations. In this paper, self-aware software agents are used to manage the distributed computations in order to improve effectiveness of investment decisions. A distributed time series forecasting approach based on the modified Group Method Data Handling (GMDH) method and agent oriented programing is proposed. The forecasted results computed by agents are used to make an investment decision. To assess the effectiveness of the system, we used the time series of EUR/USD currency pair stock prices. The empirical results with a real data set clearly suggest that the system can be deployed on the trading platform to automate process of the prediction of financial markets.