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A non-linear dynamic model for multiplicative seasonal-trend decomposition
- Source :
- Journal of Forecasting. 21:107-124
- Publication Year :
- 2002
- Publisher :
- Wiley, 2002.
-
Abstract
- A non-linear dynamic model is introduced for multiplicative seasonal time series that follows and extends the X-11 paradigm where the observed time series is a product of trend, seasonal and irregular factors. A selection of standard seasonal and trend component models used in additive dynamic time series models are adapted for the multiplicative framework and a non-linear filtering procedure is proposed. The results are illustrated and compared to X-11 and log-additive models using real data. In particular it is shown that the new procedures do not suffer from the trend bias present in log-additive models. Copyright © 2002 John Wiley & Sons, Ltd.
- Subjects :
- Series (mathematics)
Computer science
Strategy and Management
Multiplicative function
Management Science and Operations Research
Seasonality
medicine.disease
Computer Science Applications
Modeling and Simulation
Product (mathematics)
Component (UML)
medicine
Decomposition (computer science)
Econometrics
Seasonal adjustment
Statistics, Probability and Uncertainty
Selection (genetic algorithm)
Subjects
Details
- ISSN :
- 1099131X and 02776693
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- Journal of Forecasting
- Accession number :
- edsair.doi...........07dd0c0450e9027e0cabdc486b936590
- Full Text :
- https://doi.org/10.1002/for.816