An algorithm for artificial intelligence-based model adaptation to dynamic data distribution

Changes in data distribution for in-sample training and out-sample validation can be unavoidable due to presence of random dynamic noises created by external uncontrollable environmental factors. To compensate for the variation in data distribution, one approach is to recursively use immediate past prediction error to augment the current data. This paper proposes a simple algorithm that ensures the parameter settings in an ANFIS model are adaptive to its unique data distribution. Such an 'open ended' strategy allows the ANFIS to be more accurate in predicting chaotic time series problems. An application of the proposed a procedure to predict Dow Jones Industrial Average index has yielded better prediction accuracy than using the conventional prediction model.