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In this paper we propose to extend the definition of fuzzy transform in orderto consider an interpolation of models that are richer than the standard fuzzytransform. We focus on polynomial models, linear in particular, although theapproach can be easily applied to other classes of models. As an example ofapplication, we consider the smoothing of time series in finance. A comparisonwith moving averages is performed using NIFTY 50 stock market index.Experimental results show that a regression driven fuzzy transform (RDFT)provides a smoothing approximation of time series, similar to moving average,but with a smaller delay. This is an important feature for finance and otherapplication, where time plays a key role.

Regression Driven F--Transform and Application to Smoothing of Financial Time Series