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Fiorucci, Jose A.; Pellegrini, Tiago R.; Louzada, Francisco; Petropoulos, Fotios; Koehler, Anne B. (2016)
Publisher: Elsevier BV
Journal: International Journal of Forecasting
Languages: English
Types: Article
Subjects: Business and International Management
Accurate and robust forecasting methods for univariate time series are very important when the objective is to produce estimates for large numbers of time series. In this context, the Theta method’s performance in the M3-Competition caught researchers’ attention. The Theta method, as implemented in the monthly subset of the M3-Competition, decomposes the seasonally adjusted data into two “theta lines”. The first theta line removes the curvature of the data in order to estimate the long-term trend component. The second theta line doubles the local curvatures of the series so as to approximate the short-term behaviour. We provide generalisations of the Theta method. The proposed Dynamic Optimised Theta Model is a state space model that selects the best short-term theta line optimally and revises the long-term theta line dynamically. The superior performance of this model is demonstrated through an empirical application. We relate special cases of this model to state space models for simple exponential smoothing with a drift.
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    • Tiago R. Pellegrini obtained his first degree in Statistics from Federal University of São Carlos (UFSCar) in 2010, M.Sc. in Statistics from UFSCar in 2012 and is a Ph.D. student in the Department of Mathematics and Statistics at University of New Brunswick. Current research interests are related to spatial models, time series analysis and computational statistics.
    • Francisco Louzada is a Full Professor in the Institute of Mathematical Science and Computing, University of São Paulo (USP), Brazil; director for the Center for Risk Analysis, USP/UFSCar, Brazil; CNPq Productivity Researcher level 1B. He received his Ph.D. degree in Statistics from the University of Oxford, UK, his M.Sc. degree in Computational Mathematics from USP, and his B.Sc. degree from UFSCar, Brazil. His main interests are survival analysis, data mining and statistical inference. More information: http://www.mwstat.com/franciscolouzada.
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