Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Scaling up Dynamic Time Warping to Massive Dataset
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Exact indexing of dynamic time warping
Knowledge and Information Systems
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
AIASABEBI'11 Proceedings of the 11th WSEAS international conference on Applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications
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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.