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Dynamic Interaction Networks versus Local Trend Models for Multiple Time-Series Prediction.

, , and . Cybern. Syst., 42 (2): 100-123 (2011)

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A Novel Evolving Clustering Algorithm with Polynomial Regression for Chaotic Time-Series Prediction., , , , and . ICONIP (2), volume 5864 of Lecture Notes in Computer Science, page 114-121. Springer, (2009)Nearest neighbour approach with non-parametric regression analysis for multiple time-series modelling and predictions.. Int. J. Bus. Intell. Data Min., 10 (3): 253-279 (2015)Multiple Time-Series Prediction through Multiple Time-Series Relationships Profiling and Clustered Recurring Trends., , and . PAKDD (2), volume 6635 of Lecture Notes in Computer Science, page 161-172. Springer, (2011)Evolving integrated multi-model framework for on line multiple time series prediction., , and . Evol. Syst., 4 (2): 99-117 (2013)Personalised Modelling for Multiple Time-Series Data Prediction: A Preliminary Investigation in Asia Pacific Stock Market Indexes Movement., , and . ICONIP (1), volume 5506 of Lecture Notes in Computer Science, page 1237-1244. Springer, (2008)Dynamic interaction networks in modelling and predicting the behaviour of multiple interactive stock markets., , , and . Intell. Syst. Account. Finance Manag., 16 (1-2): 189-205 (2009)An extended Evolving Spiking Neural Network model for spatio-temporal pattern classification., , , , and . IJCNN, page 2653-2656. IEEE, (2011)Dynamic Interaction Networks versus Local Trend Models for Multiple Time-Series Prediction., , and . Cybern. Syst., 42 (2): 100-123 (2011)Two-stage graph-clustering algorithm and localised classification model to identify apt business locale., , , and . Int. J. Bus. Inf. Syst., 20 (2): 195-218 (2015)Localized trend model for stock market sectoral indexes movement profiling.. Algorithmic Finance, 8 (1-2): 27-46 (2019)