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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Forecasting of Short Time Series with Intelligent Computing

verfasst von : Katarzyna Kaczmarek, Olgierd Hryniewicz

Erschienen in: Challenging Problems and Solutions in Intelligent Systems

Verlag: Springer International Publishing

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Abstract

Although time series analysis and forecasting have been studied since the seventeenth century and the literature related to its statistical foundations is extensive, the problem arises when the assumptions underlying statistical modeling are not fulfilled due to the shortness of available data. In such cases, additional expert knowledge is needed to support the forecasting process. The inclusion of prior knowledge may be easily formalized with the Bayesian approach. However, the proper formulation of prior probability distributions is still one of the main challenges for practitioners. Hopefully, intelligent computing can support the formulation of the prior knowledge. In this paper, we review recent trends and challenges of the interdisciplinary research on time series forecasting with the use of intelligent computing, especially fuzzy systems. Then, we propose a method that incorporates fuzzy trends and linguistic summaries for the forecasting of short time series. Experiments show that it is a very promising and human-consistent approach.

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Vorheriges Kapitel k-Arithmetic Sequences—Theory and Applications
Nächstes Kapitel An Improved Adaptive Self-Organizing Map
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Metadaten
Titel
Forecasting of Short Time Series with Intelligent Computing
verfasst von
Katarzyna Kaczmarek
Olgierd Hryniewicz
Copyright-Jahr
2016
Buch
Challenging Problems and Solutions in Intelligent Systems

Print ISBN: 978-3-319-30164-8

Electronic ISBN: 978-3-319-30165-5

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-30165-5_4