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• W1566107402 abstract "This research proposes a pattern/shape‐similarity‐based clustering approach for time series prediction. This article uses single hidden Markov model (HMM) for clustering and combines it with soft computing techniques (fuzzy inference system/artificial neural network) for the prediction of time series. Instead of using distance function as an index of similarity, here shape/pattern of the sequence is used as the similarity index for clustering, which overcomes few of the shortcomings associated with distance‐based clustering approaches. Underlying hidden properties of time series are captured with the help of HMM. The prediction method used here exploits the pattern identification prowess of the HMM for cluster selection and the generalization and nonlinear modeling capabilities of soft computing methods to predict the output of the system. To see the validity of the proposed method in the real‐life scenario, it is tested on four different time series. The first is a benchmark Mackey–Glass time series, which is tested for delay parameters τ = 17 and τ = 30. The remaining time series are monthly sunspot data time series, Laser data time series and the last is Lorenz attractor time series. Simulation results show that the proposed method provide a better prediction performance in comparison with the existing methods." @default.
• W1566107402 created "2016-06-24" @default.
• W1566107402 creator A5033826935 @default.
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• W1566107402 date "2013-08-06" @default.
• W1566107402 modified "2023-10-16" @default.
• W1566107402 title "Pattern‐Similarity‐Based Model for Time Series Prediction" @default.
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• W1566107402 doi "https://doi.org/10.1111/coin.12015" @default.
• W1566107402 hasPublicationYear "2013" @default.
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