Fuzzy inductive reasoning (FIR) is a modelling and simulation methodology derived from the General Systems Problem Solver. It compares favourably with other soft computing methodologies, such as neural networks, genetic or neuro-fuzzy systems, and with hard computing methodologies, such as AR, ARIMA, or NARMAX, when it is used to predict future behaviour of different kinds of systems. This paper contains an overview of the FIR methodology, its historical background, and its evolution.
This paper deals with the assessment of how far into the future a time series can be safely predicted using inductive modelling and extrapolation techniques. Three different time series representing the water demand of the city of Barcelona, another characterizing the water demand of a section of the city of Rotterdam, and a third describing weather data for the city of Tucson. Fuzzy inductive reasoning (FIR) is used to predict future values of these time series on the basis of their own past. FIR predictions come with two different built-in measures of confidence that can be used to obtain a quantitative estimate of how far into the future a time series can be predicted.