A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting

DOI10.1007/s10479-014-1595-5zbMath1406.91303OpenAlexW2033885641MaRDI QIDQ893045

Ling Tang, Kaijian He, Shuai Wang, Shou-Yang Wang

Publication date: 13 November 2015

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10479-014-1595-5

zbMATH Keywords

time series analysisintelligent knowledge managementdata-characteristic-based modelingdecomposition ensemble modelnuclear energy consumption forecasting

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Economic time series analysis (91B84)

Related Items

Data-driven decision model based on local two-stage weighted ensemble learning, A novel integrated measure for energy market efficiency, The two-stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm, Spillover effect and Granger causality investigation between China's stock market and international oil market: a dynamic multiscale approach

Cites Work

• Unnamed Item
• Unnamed Item
• Nature of electron Zitterbewegung in crystalline solids
• Nuclear threat detection with mobile distributed sensor networks
• Enhanced predictions of wood properties using hybrid models of PCR and PLS with high-dimensional NIR spectral data
• A network-related nuclear power plant model with an intelligent branch- and-bound solution approach
• A confidence voting process for ranking problems based on support vector machines
• Measuring the strangeness of strange attractors
• Time series: theory and methods
• Homogeneous grouping of nuclear fuel cans through simulated annealing and tabu search
• Multilayer feedforward networks are universal approximators
• Adapting support vector machine methods for horserace odds prediction
• Crude oil price forecasting with TEI\@I methodology
• Distribution of the Estimators for Autoregressive Time Series With a Unit Root
• Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis