Regression Driven F--Transform and Application to Smoothing of Financial Time Series
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.
Keywords
Cite
@article{arxiv.1705.01941,
title = {Regression Driven F--Transform and Application to Smoothing of Financial Time Series},
author = {Luigi Troiano and Pravesh Kriplani and Irene Diaz},
journal= {arXiv preprint arXiv:1705.01941},
year = {2017}
}
Comments
IFSA-SCIS 2017, 5 pages, 6 figures, 1 table