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Enhancing Explainability of Neural Networks through Architecture Constraints
Prediction accuracy and model explainability are the two most important ...
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Novel Prediction Techniques Based on Clusterwise Linear Regression
In this paper we explore different regression models based on Clusterwis...
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Shape-Based Approach to Household Load Curve Clustering and Prediction
Consumer Demand Response (DR) is an important research and industry prob...
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Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectra
Machine Learning (ML) algorithms have been used for assessing soil quali...
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Wisdom of the crowd from unsupervised dimension reduction
Wisdom of the crowd, the collective intelligence derived from responses ...
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A connection between the pattern classification problem and the General Linear Model for statistical inference
A connection between the General Linear Model (GLM) in combination with ...
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Space Expansion of Feature Selection for Designing more Accurate Error Predictors
Approximate computing is being considered as a promising design paradigm...
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A Sparse Linear Model and Significance Test for Individual Consumption Prediction
Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well. Existing prediction methods usually have high relative errors that can be larger than 30 difficulties accounting for heterogeneity between individual users. In this paper, we propose a method to improve prediction accuracy of individual users by adaptively exploring sparsity in historical data and leveraging predictive relationship between different users. Sparsity is captured by popular least absolute shrinkage and selection estimator, while user selection is formulated as an optimal hypothesis testing problem and solved via a covariance test. Using real world data from PG&E, we provide extensive simulation validation of the proposed method against well-known techniques such as support vector machine, principle component analysis combined with linear regression, and random forest. The results demonstrate that our proposed methods are operationally efficient because of linear nature, and achieve optimal prediction performance.
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