Introduction A heuristic-based adaptive training schema (ATS) is proposed that:

1. 1 0 t -1 . . . Training intervals Testing intervals . . . Segment 1 . . . Segment i Segment s Figure 2(a): RMSE and number of rules used by MNF-ATS Figure 1(a): Adaptive Training Schema (ATS) 1 0 t -1 Training interval Testing interval Figure 2(b): Corresponding daily price changes and variance Figure 1(b): Classical Training Schema Project ID: NN0549 School of Computer Engineering Presented by: Division of Computer Science Centre for Computational Intelligence Adaptive Training Schema in Mamdani-type Neuro-Fuzzy Models for Data-Analysis in Dynamic System Forecasting • Introduction • A heuristic-based adaptive training schema (ATS) is proposed that: • is applicable in Mamdani-type neuro-fuzzy models for single-step or multi-step predictions; • is suitable for data-analysis problems that require high-levels of interpretability; and • is capable of helping fuzzy rules bases to adapt according to fundamental shifts in characteristics of time-varying systems. Methodology (Fig. 1) ATS is described as follow: • sequence the data series into s number of equal training-testing intervals; • for every segmented interval, the algorithm performs one training-testing cycle; and • comparing ATS against the classical schema, the ATS approach is better for solving problems in time-variant systems. Experimental Results (Fig. 2) ATS was applied on T+1 stock price forecasting • appears to detect these changes and adapt its rules more effectively in the stock price difference experiments; and • performs better with fewer rules as compared to the classical schema. Contributors: Javan Tan and Hiok-Chai Quek www.ntu.edu.sg