13,409 research outputs found
Neural Network Ensembles for Time Series Prediction
Rapidly evolving businesses generate massive
amounts of time-stamped data sequences and defy a demand
for massively multivariate time series analysis. For such data
the predictive engine shifts from the historical auto-regression
to modelling complex non-linear relationships between multidimensional
features and the time series outputs. In order to
exploit these time-disparate relationships for the improved time
series forecasting, the system requires a flexible methodology
of combining multiple prediction models applied to multiple
versions of the temporal data under significant noise component
and variable temporal depth of predictions. In reply
to this challenge a composite time series prediction model
is proposed which combines the strength of multiple neural
network (NN) regressors applied to the temporally varied
feature subsets and the postprocessing smoothing of outputs
developed to further reduce noise. The key strength of the model
is its excellent adaptability and generalisation ability achieved
through a highly diversified set of complementary NN models.
The model has been evaluated within NISIS Competition 2006
and NN3 Competition 2007 concerning prediction of univariate
and multivariate time-series. It showed the best predictive
performance among 12 competitive models in the NISIS 2006
and is under evaluation within NN3 2007 Competition
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A novel improved model for building energy consumption prediction based on model integration
Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into āmeta-featuresā to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%ā31.6% for Case A and 16.2%ā49.4% for Case B), generalization (improvement of 6.7%ā29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%ā34.1% for Case A and 1.8%ā19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models
Photometric redshift estimation based on data mining with PhotoRApToR
Photometric redshifts (photo-z) are crucial to the scientific exploitation of
modern panchromatic digital surveys. In this paper we present PhotoRApToR
(Photometric Research Application To Redshift): a Java/C++ based desktop
application capable to solve non-linear regression and multi-variate
classification problems, in particular specialized for photo-z estimation. It
embeds a machine learning algorithm, namely a multilayer neural network trained
by the Quasi Newton learning rule, and special tools dedicated to pre- and
postprocessing data. PhotoRApToR has been successfully tested on several
scientific cases. The application is available for free download from the DAME
Program web site.Comment: To appear on Experimental Astronomy, Springer, 20 pages, 15 figure
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