Performance Analysis of Long Short-Term Memory Predictive Neural Networks on Time Series Data
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- Adrian Sergiu Darabant & Diana Borza & Radu Danescu, 2021. "Recognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study," Mathematics, MDPI, vol. 9(2), pages 1-19, January.
- Jaime Buitrago & Shihab Asfour, 2017. "Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs," Energies, MDPI, vol. 10(1), pages 1-24, January.
- Christopher Bennett & Rodney A. Stewart & Junwei Lu, 2014. "Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks," Energies, MDPI, vol. 7(5), pages 1-23, April.
- Mohamed Abdel-Basset & Hossam Hawash & Khalid Abdulaziz Alnowibet & Ali Wagdy Mohamed & Karam M. Sallam, 2022. "Interpretable Deep Learning for Discriminating Pneumonia from Lung Ultrasounds," Mathematics, MDPI, vol. 10(21), pages 1-17, November.
- João Antunes Rodrigues & José Torres Farinha & Mateus Mendes & Ricardo J. G. Mateus & António J. Marques Cardoso, 2022. "Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition," Energies, MDPI, vol. 15(17), pages 1-16, August.
- Xiaojia Wang & Ting Huang & Keyu Zhu & Xibin Zhao, 2022. "LSTM-Based Broad Learning System for Remaining Useful Life Prediction," Mathematics, MDPI, vol. 10(12), pages 1-13, June.
- Sanda-Maria Avram & Mihai Oltean, 2022. "A Comparison of Several AI Techniques for Authorship Attribution on Romanian Texts," Mathematics, MDPI, vol. 10(23), pages 1-35, December.
- Fabio Henrique Pereira & Francisco Elânio Bezerra & Shigueru Junior & Josemir Santos & Ivan Chabu & Gilberto Francisco Martha de Souza & Fábio Micerino & Silvio Ikuyo Nabeta, 2018. "Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations," Energies, MDPI, vol. 11(7), pages 1-12, June.
- Zina Boussaada & Octavian Curea & Ahmed Remaci & Haritza Camblong & Najiba Mrabet Bellaaj, 2018. "A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation," Energies, MDPI, vol. 11(3), pages 1-21, March.
- Amin Ullah & Khalid Mahmood Malik & Abdul Khader Jilani Saudagar & Muhammad Badruddin Khan & Mozaherul Hoque Abul Hasanat & Abdullah AlTameem & Mohammed AlKhathami & Muhammad Sajjad, 2022. "COVID-19 Genome Sequence Analysis for New Variant Prediction and Generation," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
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Keywords
long short-term memory (LSTM); recurrent neural network (RNN); teacher forcing; prediction; performance analysis; benchmarking; machine learning; Tennessee Eastman process; time series;All these keywords.
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