In this paper, the j, υ corrected formulae of the amplitudes and the phases of 58 astronomical constituents are given, and the models for the analysis and prediction of 169 constituents are presented. The new Cartwri...In this paper, the j, υ corrected formulae of the amplitudes and the phases of 58 astronomical constituents are given, and the models for the analysis and prediction of 169 constituents are presented. The new Cartwright's calculated results of the tidal potential are used, and the quadratic analysis is made. It has been proved by a number of trials that the harmonic constants of constituents are more stable and the accuracy of the predicted result reliable.展开更多
We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functi...We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functional response. The proposed model naturally allows for some curvature not captured by the ordinary functional linear model. By using the proposed two-step estimating algorithm, we develop the estimates for both the link function and the regression coefficient function, and then provide predictions of new response trajectories. Besides the asymptotic properties for the estimates of the unknown functions, we also establish the consistency of the predictions of new response trajectories under mild conditions. Finally, we show through extensive simulation studies and a real data example that the proposed DSIM can highly outperform existed functional regression methods in most settings.展开更多
This paper proposes a model to analyze the massive data of electricity.Feature subset is determined by the correla-tion-based feature selection and the data-driven methods.The attribute season can be classified succes...This paper proposes a model to analyze the massive data of electricity.Feature subset is determined by the correla-tion-based feature selection and the data-driven methods.The attribute season can be classified successfully through five classi-fiers using the selected feature subset,and the best model can be determined further.The effects on analyzing electricity consump-tion of the other three attributes,including months,businesses,and meters,can be estimated using the chosen model.The data used for the project is provided by Beijing Power Supply Bureau.We use WEKA as the machine learning tool.The models we built are promising for electricity scheduling and power theft detection.展开更多
文摘In this paper, the j, υ corrected formulae of the amplitudes and the phases of 58 astronomical constituents are given, and the models for the analysis and prediction of 169 constituents are presented. The new Cartwright's calculated results of the tidal potential are used, and the quadratic analysis is made. It has been proved by a number of trials that the harmonic constants of constituents are more stable and the accuracy of the predicted result reliable.
基金supported by National Natural Science Foundation of China (Grant No. 11271080)
文摘We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functional response. The proposed model naturally allows for some curvature not captured by the ordinary functional linear model. By using the proposed two-step estimating algorithm, we develop the estimates for both the link function and the regression coefficient function, and then provide predictions of new response trajectories. Besides the asymptotic properties for the estimates of the unknown functions, we also establish the consistency of the predictions of new response trajectories under mild conditions. Finally, we show through extensive simulation studies and a real data example that the proposed DSIM can highly outperform existed functional regression methods in most settings.
基金Supported by the National Earthquake Major Project of China (201008007)the Fundamental Research Funds for Central University of China (216275645)
文摘This paper proposes a model to analyze the massive data of electricity.Feature subset is determined by the correla-tion-based feature selection and the data-driven methods.The attribute season can be classified successfully through five classi-fiers using the selected feature subset,and the best model can be determined further.The effects on analyzing electricity consump-tion of the other three attributes,including months,businesses,and meters,can be estimated using the chosen model.The data used for the project is provided by Beijing Power Supply Bureau.We use WEKA as the machine learning tool.The models we built are promising for electricity scheduling and power theft detection.
