Authors:
Fotios Petropoulos
1
and
Konstantinos Nikolopoulos
2
Affiliations:
1
Lancaster University, United Kingdom
;
2
BEM Bordeaux Management School and Bangor University, France
Keyword(s):
Forecasting Accuracy, Competitions, Theta Model, Seasonality, Time Series.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Enterprise Information Systems
;
Expert Systems
;
Formal Methods
;
Group Decision Making
;
Health Information Systems
;
Informatics in Control, Automation and Robotics
;
Information Systems Analysis and Specification
;
Intelligent Control Systems and Optimization
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Methodologies and Technologies
;
Operational Research
;
Planning and Scheduling
;
Simulation
;
Simulation and Modeling
;
Symbolic Systems
Abstract:
Forecasting accuracy and performance of extrapolation techniques has always been of major importance for both researchers and practitioners. Towards this direction, many forecasting competitions have conducted over the years, in order to provide solid performance measurement frameworks for new methods. The Theta model outperformed all other participants during the largest up-to-date competition (M3-competition). The model’s performance is based to the a-priori decomposition of the original series into two separate lines, which contain specific amount of information regarding the short-term and long-term behavior of the data. The current research investigates possible modifications on the original Theta model, aiming to the development of an optimized version of the model specifically for the monthly data. The proposed adjustments refer to better estimation of the seasonal component, extension of the decomposition feature of the original model and better optimization procedures for th
e smoothing parameter. The optimized model was tested for its efficiency in a large data set containing more than 20,000 empirical series, displaying improved performance ability when monthly data are considered.
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