Abstract
In this paper, we develop the collective of several methods of time series modeling including exponential smoothing models. We make an assumption, that the creation of complicated methods for modeling and forecasting of time series is optimal if we know all features of subject area and we able to include them in model. To improve the quality of forecasting unknown kind time series, we propose to use the aggregate results of several models. Using a fuzzy approach allows to create models with more options.
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Afanasieva, T., Yarushkina, N., Zavarzin, D., Guskov, G., Romanov, A. (2016). Time Series Forecasting Using Combination of Exponential Models and Fuzzy Techniques. In: Abraham, A., Kovalev, S., Tarassov, V., Snášel, V. (eds) Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16). Advances in Intelligent Systems and Computing, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-319-33609-1_4
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DOI: https://doi.org/10.1007/978-3-319-33609-1_4
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