Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran
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DOI: 10.1016/j.chaos.2020.110214
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References listed on IDEAS
- Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
- Wang, Jinyong & Zhang, Ce, 2018. "Software reliability prediction using a deep learning model based on the RNN encoder–decoder," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 73-82.
- Wang, Peipei & Zheng, Xinqi & Li, Jiayang & Zhu, Bangren, 2020. "Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
- Kırbaş, İsmail & Sözen, Adnan & Tuncer, Azim Doğuş & Kazancıoğlu, Fikret Şinasi, 2020. "Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
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Cited by:
- Essam A. Rashed & Akimasa Hirata, 2021. "One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
- Wang, Peipei & Liu, Haiyan & Zheng, Xinqi & Ma, Ruifang, 2023. "A new method for spatio-temporal transmission prediction of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
- Elena G. Popkova & Aleksei V. Bogoviz & Svetlana V. Lobova & Abdula M. Chililov & Anastasia A. Sozinova & Bruno S. Sergi, 2022. "Changing entrepreneurial attitudes for mitigating the global pandemic’s social drama," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-12, December.
- Abbasimehr, Hossein & Paki, Reza, 2021. "Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
- Lv, Yang & Lv, Zhiqiang & Cheng, Zesheng & Zhu, Zhanqi & Rashidi, Taha Hossein, 2023. "TS-STNN: Spatial-temporal neural network based on tree structure for traffic flow prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
- Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Ho, Andrew Fu Wah & Liu, Nan & Ong, Marcus Eng Hock & Cheong, Kang Hao, 2022. "A deep learning architecture for forecasting daily emergency department visits with acuity levels," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
- Bulut Boru & M. Emre Gursoy, 2022. "Forecasting Daily COVID-19 Case Counts Using Aggregate Mobility Statistics," Data, MDPI, vol. 7(11), pages 1-24, November.
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Keywords
Covid-19; LSTM; Rolling update mechanism; Modeling; Forecasting;All these keywords.
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