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2016 Bayesian nonparametric forecasting of monotonic functional time series
Antonio Canale, Matteo Ruggiero
Electron. J. Statist. 10(2): 3265-3286 (2016). DOI: 10.1214/16-EJS1190


We propose a Bayesian nonparametric approach to modelling and predicting a class of functional time series with application to energy markets, based on fully observed, noise-free functional data. Traders in such contexts conceive profitable strategies if they can anticipate the impact of their bidding actions on the aggregate demand and supply curves, which in turn need to be predicted reliably. Here we propose a simple Bayesian nonparametric method for predicting such curves, which take the form of monotonic bounded step functions. We borrow ideas from population genetics by defining a class of interacting particle systems to model the functional trajectory, and develop an implementation strategy which uses ideas from Markov chain Monte Carlo and approximate Bayesian computation techniques and allows to circumvent the intractability of the likelihood. Our approach shows great adaptation to the degree of smoothness of the curves and the volatility of the functional series, proves to be robust to an increase of the forecast horizon and yields an uncertainty quantification for the functional forecasts. We illustrate the model and discuss its performance with simulated datasets and on real data relative to the Italian natural gas market.


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Antonio Canale. Matteo Ruggiero. "Bayesian nonparametric forecasting of monotonic functional time series." Electron. J. Statist. 10 (2) 3265 - 3286, 2016.


Received: 1 December 2015; Published: 2016
First available in Project Euclid: 16 November 2016

zbMATH: 1357.62278
MathSciNet: MR3572849
Digital Object Identifier: 10.1214/16-EJS1190

Primary: 62G00
Secondary: 60J22 , 62F15

Keywords: Approximate Bayesian Computation , dependent processes , Dirichlet process , Interacting particle system , Moran model , Polya urn , prediction

Rights: Copyright © 2016 The Institute of Mathematical Statistics and the Bernoulli Society


Vol.10 • No. 2 • 2016
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