Forecasting irregularly spaced data: An extension of double exponential smoothing |
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| Affiliation: | 1. Institute of Mathematics, University of Zürich, Zürich, Switzerland;2. School of Mathematics, Alan Turing Building, University of Manchester, Oxford Road, Manchester, UK;1. Institute for Digital Communications, School of Engineering, The University of Edinburgh, King''s Buildings, Thomas Bayes Road, EH9 3FG Edinburgh, UK;2. Faculty of Science, University of Copenhagen, Rolighedsvej 30, DK-1958 Frederiksberg C, Denmark;3. Departamento de Psiquiatría y Psicología Médica, Complutense University of Madrid, Madrid, Spain;4. Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain;5. Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain;1. Space Power Technology State Key Laboratory, Shanghai Institute of Space Power-Sources, Shanghai 200245, China;2. Department of Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;3. Northwest Institute of Nuclear Technology, Xi’an 710024, China;1. Office of Research, Endeavour College of Natural Health, Fortitude Valley, Queensland, Australia;2. Australian Research Centre in Complementary and Integrative Medicine, Faculty of Health, University of Technology Sydney, Ultimo, New South Wales, Australia;3. Faculty of Medicine and Health, University of New England, Armidale, New South Wales, Australia;4. Naturopaths and Herbalists Association of Australia, Ashfield, New South Wales, Australia;1. College of Mathematics and Informatics, Center for Applied Mathematics of Fujian Province & FJKLMAA, Fujian Normal University, Fuzhou, Fujian Province, 350117, PR China;2. College of Mathematics and Informatics & Center for Applied Mathematics of Fujian Province, Fujian Normal University, Fuzhou, Fujian Province, 350117, PR China |
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| Abstract: | Automated forecasts are often required, in practice, using data series from which certain points are missing and from data occurring at completely irregular time intervals. For instance, in computerised inventory control, fast methods of dealing with such data are required. There is an almost complete absence in the literature of computationally efficient methods for such a situation. This paper gives an extension of single and double exponential smoothing adapted to data occurring at irregular time intervals. These extensions are shown to have modest computational requirements and little sensitivity to initial conditions. Results of tests on sample data series are given showing only a minor decrease in accuracy with missing data, and indicating the appropriate method of choosing the smoothing parameter. Application of this method to published government time series is illustrated by two examples, firstly, to river water quality data originating from samples taken at irregular time intervals and, secondly, to divorce rate statistics from which certain points are missing due to summarizing the data. Successive summarizing of these series is found to have a negligible effect on forecast accuracy implying attractive cost saving possibilities in data collection and publication. |
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