2017/05/04 by Luigi Troiano, Troiano, Luigi, Pravesh Kriplani +4
Computer Science · Physics and Astronomy · #Computational Engineering #Data Analysis #Discrete Mathematics (cs.DM) #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Fuzzy Logic and Control Systems #Rough Sets and Fuzzy Logic #Statistics and Probability (physics.data-an) #Time Series Analysis and Forecasting #and Science (cs.CE) #cs.CE #cs.DM #physics.data-an
paper · pdf · doi:10.48550/arxiv.1705.01941
IFSA-SCIS 2017, 5 pages, 6 figures, 1 table
arxiv created 2017/05/04 · openalex publication_date 2017/05/04 · arxiv updated 2017/05/08 · openalex created_date 2022/10/23 · openalex updated_date 2026/07/28
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.