Fuzzy TS Model Example with Implementation Details

This example demonstrates the implementation of a fuzzy TS model, providing valuable insights for those new to TS modeling. The fuzzy Takagi-Sugeno (TS) model is a time series forecasting approach based on fuzzy logic principles. Key implementation aspects include: fuzzification of time series data using membership functions, rule base construction with linear consequent parts, and defuzzification methods for crisp output generation. Through fuzzy processing of time series data, the model effectively handles real-world complexity and uncertainty, making it widely applicable in practical scenarios. Beginners can utilize this example to understand fundamental TS model concepts, including algorithm flow (data preprocessing → fuzzy partitioning → rule learning → parameter optimization → prediction), while gaining hands-on experience with MATLAB implementations using functions like genfis for rule generation and anfis for model training. This foundation supports further research and application development in fuzzy time series forecasting.