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

## TL;DR

This paper introduces a regression driven fuzzy transform (RDFT) for smoothing financial time series, offering a method that reduces delay compared to traditional moving averages, with potential benefits for finance applications.

## Contribution

The paper extends fuzzy transform to include polynomial models, specifically linear, and demonstrates its effectiveness in smoothing financial data with less delay.

## Key findings

- RDFT provides smoothing similar to moving averages
- RDFT has smaller delay than moving averages
- Experimental results on NIFTY 50 show improved performance

## Abstract

In this paper we propose to extend the definition of fuzzy transform in order to consider an interpolation of models that are richer than the standard fuzzy transform. We focus on polynomial models, linear in particular, although the approach can be easily applied to other classes of models. As an example of application, we consider the smoothing of time series in finance. A comparison with 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 other application, where time plays a key role.