Chaotic Time Series Prediction Using a Neuro-Fuzzy System with Time-Delay Coordinates

Abstract

This paper presents an investigation into the use of the delay coordinate embedding technique in the multi-input- multioutput-adaptive-network-based fuzzy inference system (MANFIS) for chaotic time series prediction. The inputs to the MANFIS are embedded-phase-space (EPS) vectors preprocessed from the time series under test, while the output time series is extracted from the output EPS vectors from the MANFIS. A moving root-mean-square error is used to monitor the error over the prediction horizon and to tune the membership functions in the MANFIS. With the inclusion of the EPS preprocessing step, the prediction performance of the MANFIS is improved significantly. The proposed method has been tested with one periodic function and two chaotic functions including Mackey-Glass chaotic time series and Duffing forced-oscillation system. The prediction performances with and without EPS preprocessing are statistically compared by using the t-test method. The results show that EPS preprocessing can help improve the prediction performance of a MANFIS significantly.