ENBIS-17 in Naples
9 – 14 September 2017; Naples (Italy)
Abstract submission: 21 November 2016 – 10 May 2017
Functional Data Analysis for Business and Industry
12 September 2017, 12:40 – 13:10
- Submitted by
- Simone Vantini
- Simone Vantini (MOX - Dept. of Mathematics, Politecnico di Milano)
- The continuous and outstanding advances of measurement technologies have enabled the collection and storage of high-resolution data which can often be modeled as smooth functions (e.g., curves or surfaces). This kind of data are at the basis of functional data analysis (FDA, Ramsay and Silverman, 2005) which is a well-known lively and expanding research area of modern statistics. In FDA, the classical concept for scalar or multivariate random variable is indeed replaced by the concept of functional random variable. Consequently, in FDA the typical data set is not made of numbers or Euclidean vectors but a collection of functions embedded in a suitable separable functional Hilbert space meant to formalize application-specific relations between sample units.
Recent applications of FDA techniques in different and many fields of science are countless. Nevertheless, very few business and industrial applications can be found, thus pointing out the existence of an unexploited potential of this type of techniques in these two fields.
With respect to this discrepancy, this talk will showcase two recent business and industrial applications in which state-of-the-art FDA techniques are fruitfully used. The first application (i.e., Canale and Vantini, 2016) pertains to the one-day-ahead prediction of natural gas demand and supply curves in the Italian gas balancing platform (i.e., Mercato del Bilanciamento). In this former application, the concept of constrained functional auto-regressive model is introduced and then used for predictive purposes. The latter application (Pini et al., 2017) pertains instead to the real-time monitoring of a laser-based welding process based on the analysis of plasma/metal emission spectrum. In this application, a local non-parametric functional ANOVA is performed such to build ad-hoc monitoring tools for the early detection of out-of-control dynamics.
Canale, A. and Vantini, S. (2016): " Constrained functional time series: Applications to the Italian gas market”, International Journal of Forecasting, Vol. 32(4), pp. 1340-1351.
Pini, A., Vantini, S., Colosimo, B. M., Grasso, M. (2017): “Domain-Selective Functional ANOVA for Supervised Statistical Profile Monitoring of Signal Data”, Journal of the Royal Statistical Society – Series C (to appear).
Ramsay, J. O. and Silverman, B. W. (2005): Functional data analysis, Springer, New York.
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