Ensemble deep learning for regression and time series forecasting

By Xueheng Qiu, Le Zhang, Ye Ren, Ponnuthurai Nagaratnam Suganthan, Gehan A. J. Amaratunga. 2014

353 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Optimization & Training, Signal Processing, Time-Series & Forecasting

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, background): “The learning is performed multiple times on the network [65].”
Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “Few works tried to enrich DNNs with regression capabilities, such as the work in [219] proposing the ensemble of DBN and Support Vector Regression (SVR)”
Deep Learning for Financial Applications : A Survey (2020, background): “RBM is a different type of ANN model that can learn the probability distribution of the input set [29].”
Deep neural network based demand side short term load forecasting (2016, methodology): “In [9], DNN is used for time series wind forecasting, and DBN was used as a part of ensemble learning in [10].”
A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects (2022, methodology): “Furthermore, ensemble learning methods have been com-1264 bined with deep learning for different applications, includ-1265 ing human activity recognition [174], time series forecasting [175], disease prediction [137], [176], [177], wind speed prediction [178], and outlier detection [179].”
Ensemble learning: A survey (2018, background): “Qiu, Zhang, Ren, Suganthan, and Amaratunga (2014) presented an ensemble of DNNs for regression and time series forecasting.”
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting (2018, methodology): “[26] applied an ensemble of DBNs aggregated together by a Support Vector Regression (SVR) model to the problem of energy load forecasting.”
Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting (2017, methodology): “Among the various ensemble methods [26, 27, 28, 29, 30], divide and conquer [31] is a concept which is often applied in time series forecasting.”
A type of generalization error induced by initialization in deep neural networks (2019, background): “, 2018), prediction of time series (Qiu et al., 2014) and solving differential equations (E and Yu, 2018; Xu et al.”
Deep neural networks for energy load forecasting (2017, methodology): “In addition to these methods, authors in [22] and in [23] presented Deep Belief Networks for load forecasting.”
A Time Series Forecasting Model Based on Deep Learning Integrated Algorithm with Stacked Autoencoders and SVR for FX Prediction (2016, methodology): “Deep learning algorithms trained in this fashion have been shown empirically to avoid getting stuck in the kind of poor solutions one typically reaches with only random initialization[5-6].”
A comprehensive review on ensemble deep learning: Opportunities and challenges (2023, background): “For instance, a stacking ensemble learning sys-tem was proposed by Divina et al. (2018) to forecast electric energy usage in Spain and Qiu et al. (2014) to forecast electric energy usage in Australia.”
Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting (2016, methodology): “used an ensemble of DBNs coupled with a traditional SVM-based regression technique [42].”