Time Series Analysis, Modeling and Applications A Computational Intelligence Perspective /
Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Int...
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| Other Authors: | , |
| Format: | Electronic |
| Language: | English |
| Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2013.
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| Series: | Intelligent Systems Reference Library,
47 |
| Subjects: | |
| Online Access: | https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-33439-9 |
Table of Contents:
- From the Contents: The links between statistical and fuzzy models for time series analysis and forecasting
- Incomplete time series: imputation through Genetic Algorithms
- Intelligent aggregation and time series smoothing
- Financial fuzzy Time series models based on ordered fuzzy numbers
- Stochastic-fuzzy knowledge-based approach to temporal data modeling.-A Novel Choquet integral composition forecasting model for time series data based on completed� extensional L-measure
- An application of enhanced knowledge models� to fuzzy time series
- A wavelet transform approach to chaotic short-term forecasting
- Fuzzy forecasting with fractal analysis for the time series of environmental pollution
- Support vector regression with kernel Mahalanobis measure for financial forecast.