Time Series Prediction Using Fuzzy Inductive Reasoning: A Case Study

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Abstract

This paper presents the application of fuzzy inductive reasoning (FIR) to time-series analysis and forecasting. This methodology had previously been applied to modeling and control of dynamic input-output systems. The research effort discussed in this paper presents a first attempt at assessing the suitability of this qualitative modeling methodology for forecasting time-series, i.e., for predicting the future development of signals on the basis of their own past, without identifying the systems that produce these signals. The performance of FIR in time-series forecasting is compared with Box-Jenkins methods and neural networks in a case study.

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Last modified: November 29, 2006 -- © François Cellier