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- W1589804540 abstract "This paper proposes a mix of noise filtering, fuzzy clustering, neural mapping and predictive techniques for one-subsequence-ahead forecasting of nonstationary time series. Optionally, we may start with de-noising the time series by wavelet decomposition. A non-overlapping subsequence time series clustering procedure with a sliding window is next addressed, by using a lower-bound of the Dynamic Time Warping distance as a dissimilarity measure, when applying the Fuzzy C-Means algorithm. Afterwards, the subsequence time series transition function is learned by neural mapping, consisting of deriving, for each subsequence time series, the degrees to which it belongs to the c cluster prototypes, when the pċc membership degrees of the previous p subsequences are presented as inputs to the neural network. Finally, this transition function is applied to forecasting one-subsequence-ahead time series, as a weighted mean of the c cluster prototypes to which it belongs, and the S&P 500 data are used for testing." @default.
- W1589804540 created "2016-06-24" @default.
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- W1589804540 date "2010-01-01" @default.
- W1589804540 modified "2023-09-26" @default.
- W1589804540 title "A Computational Intelligence Based Framework for One-Subsequence-Ahead Forecasting of Nonstationary Time Series" @default.
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- W1589804540 doi "https://doi.org/10.1007/978-3-642-16292-3_19" @default.
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