We describe in this article a new hybrid fuzzy-fractal approach for plant monitoring. We use the concept of the fractal dimension to measure the complexity of a time series of observed data from the plant. We also use fuzzy logic to represent expert knowledge on monitoring the process in the plant.
A hybrid forecasting approach for piece-wise stationary time series
✍ Scribed by Minxian Yang; Ronald Bewley
- Publisher
- John Wiley and Sons
- Year
- 2006
- Tongue
- English
- Weight
- 208 KB
- Volume
- 25
- Category
- Article
- ISSN
- 0277-6693
- DOI
- 10.1002/for.1003
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✦ Synopsis
Abstract
We consider the problem of forecasting a stationary time series when there is an unknown mean break close to the forecast origin. Based on the intercept‐correction methods suggested by Clements and Hendry (1998) and Bewley (2003), a hybrid approach is introduced, where the break and break point are treated in a Bayesian fashion. The hyperparameters of the priors are determined by maximizing the marginal density of the data. The distributions of the proposed forecasts are derived. Different intercept‐correction methods are compared using simulation experiments. Our hybrid approach compares favorably with both the uncorrected and the intercept‐corrected forecasts. Copyright © 2006 John Wiley & Sons, Ltd.
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