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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: IEEE Press
Languages: English
Types: Unknown
Subjects: aintel, csi
Rapidly evolving businesses generate massive\ud amounts of time-stamped data sequences and defy a demand\ud for massively multivariate time series analysis. For such data\ud the predictive engine shifts from the historical auto-regression\ud to modelling complex non-linear relationships between multidimensional\ud features and the time series outputs. In order to\ud exploit these time-disparate relationships for the improved time\ud series forecasting, the system requires a flexible methodology\ud of combining multiple prediction models applied to multiple\ud versions of the temporal data under significant noise component\ud and variable temporal depth of predictions. In reply\ud to this challenge a composite time series prediction model\ud is proposed which combines the strength of multiple neural\ud network (NN) regressors applied to the temporally varied\ud feature subsets and the postprocessing smoothing of outputs\ud developed to further reduce noise. The key strength of the model\ud is its excellent adaptability and generalisation ability achieved\ud through a highly diversified set of complementary NN models.\ud The model has been evaluated within NISIS Competition 2006\ud and NN3 Competition 2007 concerning prediction of univariate\ud and multivariate time-series. It showed the best predictive\ud performance among 12 competitive models in the NISIS 2006\ud and is under evaluation within NN3 2007 Competition.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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