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• W2463151718 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." @default.
• W2463151718 created "2016-07-22" @default.
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• W2463151718 date "2016-01-01" @default.
• W2463151718 modified "2023-09-24" @default.
• W2463151718 title "Forecasting of Short Time Series with Intelligent Computing" @default.
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• W2463151718 doi "https://doi.org/10.1007/978-3-319-30165-5_4" @default.
• W2463151718 hasPublicationYear "2016" @default.
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