Journal
KNOWLEDGE-BASED SYSTEMS
Volume 160, Issue -, Pages 188-199Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.knosys.2018.06.033
Keywords
Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); Empirical mode decomposition (EMD); Nonlinear autoregressive with exogenous inputs (NARX) neural network; Weight function
Categories
Funding
- National Natural Science Foundation of China [71501138, 71371130, 71501019]
- Construction Plan of Scientific Research Innovation Team for Colleges and Universities in Sichuan Province [15TD0004]
- Funding Program for Middle-aged Core Teachers of Chengdu University of Technology [KYGG201519]
- Key Program of Resource-based City Development Research Center [ZYZX-ZD-1701]
- Special Funding for Post-doctoral Research Project of Sichuan in 2017 Named 'Dynamic Evolution of Multi-system Coupling in Resource-Oriented Cities of Western China from Technology Innovation-Driven Perspective'
- China's Post-doctoral Science Fund Project [2018M631069]
- Philosophy and Social Science Planning Program of Chengdu [2018A09]
- Scientific Research Project in Sichuan Province Department of Education [16SB0071]
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In recent years, empirical mode decomposition based models for signal analysis and prediction have been introduced into a various fields such as electricity loading, crude oil pricing, wind speed assessment, energy consumption, foreign exchange rates, and tourist arrivals, and have shown good performances for both nonlinear and non-stationary time series predictions. This study incorporates a nonlinear autoregressive neural network with exogenous inputs (NARX) into a decomposition based forecasting framework to propose a weighted recombination model for one-step ahead forward predictions. This proposed model is based on the assumption that as the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) model derives different components from the given data series, it makes different contributions to the final prediction results. A weight function is therefore introduced to determine suitable weights for each individual prediction result derived from the decomposed components and the corresponding NARX model. Finally, a new weighted decomposition based forecasting model is developed that is combined with the NARX model and the weight function. To justify and compare the effectiveness of the new proposed model, two non-linear, non-stationary data series are applied as the data resource for numerical experiments and 12 commonly used non-decomposition or decomposition based prediction models are selected as benchmarks, from which it is demonstrated that compared with the 12 models, the proposed new model noticeably improves forecasting accuracy.
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