Summary
Uncertainties in the real world often appear as variabilities of observed data under similar conditions. In this paper, we use interval functions to model uncertainty and function volatility. To estimate such kinds of functions, we propose a practical interval function approximation algorithm. Applying this algorithm, we have studied stock market forecasting with real economic data from 1930-2004. The computational results indicate that interval function approximation can produce better quality forecasts than that obtained with other methods.
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Hu, C. (2008). Using Interval Function Approximation to Estimate Uncertainty. In: Huynh, VN., Nakamori, Y., Ono, H., Lawry, J., Kreinovich, V., Nguyen, H.T. (eds) Interval / Probabilistic Uncertainty and Non-Classical Logics. Advances in Soft Computing, vol 46. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77664-2_26
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DOI: https://doi.org/10.1007/978-3-540-77664-2_26
Publisher Name: Springer, Berlin, Heidelberg
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