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Year 2021, Volume: 7 Issue: 1, 117 - 126, 29.06.2021

Osman Tayfun Bişkin

https://doi.org/10.22531/muglajsci.875414

Abstract

References

  • Lu, H., Stratton, C. W. and Tang, Y.-W., "Outbreak of pneumonia of un-known etiology in wuhan china: the mystery and the miracle", Jour.Med. Virol. 92 (4), 401–402, 2020.
  • Sohrabi, C., Alsafi, Z., O’Neill, N., Khan, M., Kerwan, A., Al-Jabir, A, Iosifidis C. and Agha, R., "World Health Organization Declares Global Emergency: A Review of The 2019 Novel Coronavirus (Covid-19)", Int. Jour. Surgery, 76, 71–76, 2020.
  • World Health Organization, Archived: Who timeline -covid-19, Retrieved from: https://www.who.int/news/item/27-04-2020-who-timeline---covid-19(21/11/2020).
  • Ribeiro, M. H. D. M., Silva, R. G., Mariani, V. C., Coelho, L. S., "Short-Term Forecasting Covid-19 Cumulative Confirmed Cases: Perspectives for Brazil", Chaos, Solit. and Frac., 135, 109853, 2020.
  • Zeroual, A., Harrou, F., Dairi, A. and Sun, Y., "Deep Learning Methods Forforecasting Covid-19 Time-Series Data: A Comparative Study", Chaos,Solit. and Frac., 140, 110121, 2020.
  • Chimmula, V. K. R. and Zhang, L., "Time Series Forecasting of Covid-19 Transmission in Canada Using LSTM Networks", Chaos, Solit. and Frac., 135, 109864, 2020.
  • F. Shahid, F., Zameer, A. and Muneeb, M., "Predictions For Covid-19 With Deep Learning Models of LSTM, GRU and Bi-LSTM", Chaos, Solit. and Frac., 140, 110212, 2020.
  • Kırbaş, İ., Sözen, A, Tuncer, A. D. and Kazancıoğlu, F. S. "Comparative Analysis and Forecasting Of Covid-19 Cases in Various European Countries with ARIMA, NARNN and LSTM Approaches", Chaos, Solit. and Frac., 138, 110015, 2020.
  • Arora, P., Kumar, H. and Panigrahi, B., "Prediction and Analysis of Covid-19 Positive Cases Using Deep Learning Models: A Descriptive Case Study of India", Chaos, Solit. and Frac., 139, 110017, 2020.
  • Sarkar, K., Khajanchi, S. and Nieto, J., "Modeling and Forecasting the Covid-19 Pandemic in India", Chaos, Solit. and Frac., 139, 110049, 2020.
  • Yang, Q., Yi, C., Vajdi, A., Cohnstaedt, L. W., Wu, H., Guo, X. and Scoglio, C. M., "Short-Term Forecasts and Long-Term Mitigation Evaluations for the Covid-19 Epidemic in Hubei Province, China", Infectious Disease Modelling, 5, 563–574, 2020.
  • Wang, P., Zheng, X., Li, J. and Zhu, B.,"Prediction of Epidemic Trends in Covid-19 with Logistic Model and Machine Learning Technics", Chaos,Solit. and Frac., 139, 110058, 2020.
  • Salgotra, R., Gandomi, M. and Gandomi, A., "Time Series Analysis and Forecast of the Covid-19 Pandemic in India Using Genetic Programming", Chaos, Solit. and Frac. 138, 109945, 2020.
  • Rustam, F., Reshi, A. A., Mehmood, A., Ullah, S., On, B.-W., Aslam, W. and Choi, A. G. S.,"Covid-19 Future Forecasting Using Supervised Machine Learning Models", IEEE Access, 8, 101489 – 101499, 2020.
  • Cao, L., Liu, H., Li, J., Yin, X., Duan, Y. and Wang, J., "Relationship of Meteorological Factors and Human Brucellosis in Hebei Province, China", Sci. Total Envir., 703, 135491, (2020).
  • He, Z. and Tao, H., "Epidemiology and Arima Model Of Positive-Rate of Influenza Viruses Among Children in Wuhan, China: A Nine-Year Retrospective Study", Int. Jour. Infect. Dis., 74, 61–70, 2018.
  • Ceylan, Z., "Estimation of Covid-19 Prevalence in Italy, Spain, and France", Sci. Total Environ. 729(4), 1–7, 2020.
  • Tomar, A. and Gupta, A., "Prediction for the Spread of Covid-19 in India and Effectiveness of Preventive Measures", Sci. Total Envir., 728, 138762, (2020).
  • Wan, R., Mei, S., Wang, J., Liu, M. and Yang, F., "Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting", electronics, 8(8):876, 1–18, 2019.
  • Gan, Z., Li, C., Zhou, J. and Tang, G., "Temporal Convolutional Networks Interval Prediction Model for Wind Speed Forecasting", Electric Power Sys. Res., 191, 106865, 2021.
  • Lara-Benítez, P., Carranza-García, M., Luna-Romera, J. M. and Riquelme, J. C., "Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting", applied sciences, 10, 1–17, 2020.
  • Pelletier, C., Webb, G. I. and Petitjean, F., "Temporal Convolutional Neuralnetwork for the Classification of Satellite Image Time Series", remotesensing, 11(5):523, 1–22, 2019.
  • Guo, G. and Yuan, W., "Short-Term Traffic Speed Forecasting Based on Graph Attention Temporal Convolutional Networks", Neurocomputing, 410, 387–393, 2020.
  • Li, S., Farha, Y. A., Liu, Y., Cheng, M.-M. and Gall, J.,"MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation", IEEE Trans. Patt. Anal. Mach. Intel., Early Access, 1–13, 2020.
  • Kok, C., Jahmunah, V., Oh, S. L., Zhou, X., Gururajan, R., Tao, X., Cheong, K. H., Gururajan, R., Molinari, F. and Acharya, U. R., "Automated Prediction of Sepsis Using Temporal Convolutional Network", Comp. Bio. Med., 1–10, 2020.
  • Yan, J., Mu, L., Wang, L., Ranjan, R. and Zomaya, A. Y., "Temporal Convolutional Networks For The Advance Prediction Of ENSO", Scientific Reports (Nature Publisher Group), 10, 1–15, 2020.
  • Hochreiter, S. and Schmidhuber, J., "Long Short-Term Memory", Neural Computation, 9(8), 1735–1780, 1997.
  • Gers, F. A., Schmidhuber, J., Cummins, F., "Learning to Forget: Continual Prediction with LSTM", Neural Computation, 12(10), 2451–2471, 2000.
  • Chung, J., Gulcehre, C., Cho, K., Bengio, Y., "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling", arXiv preprint, arXiv:1412.3555, 1–9, 2014.
  • Cho, K., Van Merriënboer, B., Bahdanau, D. and Bengio, Y., "On the Properties of Neural Machine Translation: Encoder-Decoder Approaches", arXiv preprint, arXiv:1409.1259, 2014.
  • Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. and Kavukcuoglu, K., "Wavenet: A Generative Model for Raw Audio", arXiv preprint, arXiv:1609.03499, 1–15, 2016.
  • Hewage, P., Behera, A., Trovati, M., Pereira, E., Ghahremani, M., Palmieri, F. and Liu, Y., "Temporal Convolutional Neural (TCN) Network for an Effective Weather Forecasting Using Time-Series Data from the Local Weather Station", Methodologies and Application, 24, 16453–16482, 2020.
  • Zhen, X., Chakraborty, R., Vogt, N., Bendlin, B. B. and Singh, V., "Dilated Convolutional Neural Networks For Sequential Manifold-valued Data", Proceedings of the IEEE/CVF International Conference on Computer Vision, 10621–10631, 2019.
  • European Centre for Disease Prevention and Control, Geographic Distribution of Covid-19 Cases, Retrieved from: https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide 08/12/2020.
  • Kingma, D. P. and Ba, J. L., "Adam: A Method For Stochastic Optimization", arXiv preprint, arXiv:1412.6980, 2014.

MULTI-STEP FORECASTING OF COVID-19 CASES IN EUROPEAN COUNTRIES USING TEMPORAL CONVOLUTIONAL NETWORKS

Year 2021, Volume: 7 Issue: 1, 117 - 126, 29.06.2021

Osman Tayfun Bişkin

https://doi.org/10.22531/muglajsci.875414

Abstract

The novel Coronavirus (COVID-19) has significantly affected millions of people around the world since the first notification until nowadays. The rapid spread of the virus has dramatically increased the workload of healthcare systems in many countries. Therefore, the need for efficient use of the healthcare system leads researchers to forecast the trend of virus spread. For this purpose, Machine Learning (ML) and Artificial Intelligence (AI) applications have intensively used to struggle against the coronavirus outbreak. In this study, Temporal Convolutional Network (TCN) is applied for modeling the cumulative confirmed COVID-19 cases and forecasting the spread of it in various European countries using time series data. It is also presented that numerical examples for comparing performances of TCN against Long-Short Term Memory (LSTM) and Gates Recurrent Units(GRU) in terms of computation time, root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), root mean squared log error (RMSLE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE). Simulation results indicate that the Temporal Convolutional Networks used in this manuscript performs better than other models for forecasting the cumulative confirmed COVID-19 cases.

Keywords

COVID-19 Modeling Forecasting Machine Learning Deep Learning GRU LSTM TCN

References

  • Lu, H., Stratton, C. W. and Tang, Y.-W., "Outbreak of pneumonia of un-known etiology in wuhan china: the mystery and the miracle", Jour.Med. Virol. 92 (4), 401–402, 2020.
  • Sohrabi, C., Alsafi, Z., O’Neill, N., Khan, M., Kerwan, A., Al-Jabir, A, Iosifidis C. and Agha, R., "World Health Organization Declares Global Emergency: A Review of The 2019 Novel Coronavirus (Covid-19)", Int. Jour. Surgery, 76, 71–76, 2020.
  • World Health Organization, Archived: Who timeline -covid-19, Retrieved from: https://www.who.int/news/item/27-04-2020-who-timeline---covid-19(21/11/2020).
  • Ribeiro, M. H. D. M., Silva, R. G., Mariani, V. C., Coelho, L. S., "Short-Term Forecasting Covid-19 Cumulative Confirmed Cases: Perspectives for Brazil", Chaos, Solit. and Frac., 135, 109853, 2020.
  • Zeroual, A., Harrou, F., Dairi, A. and Sun, Y., "Deep Learning Methods Forforecasting Covid-19 Time-Series Data: A Comparative Study", Chaos,Solit. and Frac., 140, 110121, 2020.
  • Chimmula, V. K. R. and Zhang, L., "Time Series Forecasting of Covid-19 Transmission in Canada Using LSTM Networks", Chaos, Solit. and Frac., 135, 109864, 2020.
  • F. Shahid, F., Zameer, A. and Muneeb, M., "Predictions For Covid-19 With Deep Learning Models of LSTM, GRU and Bi-LSTM", Chaos, Solit. and Frac., 140, 110212, 2020.
  • Kırbaş, İ., Sözen, A, Tuncer, A. D. and Kazancıoğlu, F. S. "Comparative Analysis and Forecasting Of Covid-19 Cases in Various European Countries with ARIMA, NARNN and LSTM Approaches", Chaos, Solit. and Frac., 138, 110015, 2020.
  • Arora, P., Kumar, H. and Panigrahi, B., "Prediction and Analysis of Covid-19 Positive Cases Using Deep Learning Models: A Descriptive Case Study of India", Chaos, Solit. and Frac., 139, 110017, 2020.
  • Sarkar, K., Khajanchi, S. and Nieto, J., "Modeling and Forecasting the Covid-19 Pandemic in India", Chaos, Solit. and Frac., 139, 110049, 2020.
  • Yang, Q., Yi, C., Vajdi, A., Cohnstaedt, L. W., Wu, H., Guo, X. and Scoglio, C. M., "Short-Term Forecasts and Long-Term Mitigation Evaluations for the Covid-19 Epidemic in Hubei Province, China", Infectious Disease Modelling, 5, 563–574, 2020.
  • Wang, P., Zheng, X., Li, J. and Zhu, B.,"Prediction of Epidemic Trends in Covid-19 with Logistic Model and Machine Learning Technics", Chaos,Solit. and Frac., 139, 110058, 2020.
  • Salgotra, R., Gandomi, M. and Gandomi, A., "Time Series Analysis and Forecast of the Covid-19 Pandemic in India Using Genetic Programming", Chaos, Solit. and Frac. 138, 109945, 2020.
  • Rustam, F., Reshi, A. A., Mehmood, A., Ullah, S., On, B.-W., Aslam, W. and Choi, A. G. S.,"Covid-19 Future Forecasting Using Supervised Machine Learning Models", IEEE Access, 8, 101489 – 101499, 2020.
  • Cao, L., Liu, H., Li, J., Yin, X., Duan, Y. and Wang, J., "Relationship of Meteorological Factors and Human Brucellosis in Hebei Province, China", Sci. Total Envir., 703, 135491, (2020).
  • He, Z. and Tao, H., "Epidemiology and Arima Model Of Positive-Rate of Influenza Viruses Among Children in Wuhan, China: A Nine-Year Retrospective Study", Int. Jour. Infect. Dis., 74, 61–70, 2018.
  • Ceylan, Z., "Estimation of Covid-19 Prevalence in Italy, Spain, and France", Sci. Total Environ. 729(4), 1–7, 2020.
  • Tomar, A. and Gupta, A., "Prediction for the Spread of Covid-19 in India and Effectiveness of Preventive Measures", Sci. Total Envir., 728, 138762, (2020).
  • Wan, R., Mei, S., Wang, J., Liu, M. and Yang, F., "Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting", electronics, 8(8):876, 1–18, 2019.
  • Gan, Z., Li, C., Zhou, J. and Tang, G., "Temporal Convolutional Networks Interval Prediction Model for Wind Speed Forecasting", Electric Power Sys. Res., 191, 106865, 2021.
  • Lara-Benítez, P., Carranza-García, M., Luna-Romera, J. M. and Riquelme, J. C., "Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting", applied sciences, 10, 1–17, 2020.
  • Pelletier, C., Webb, G. I. and Petitjean, F., "Temporal Convolutional Neuralnetwork for the Classification of Satellite Image Time Series", remotesensing, 11(5):523, 1–22, 2019.
  • Guo, G. and Yuan, W., "Short-Term Traffic Speed Forecasting Based on Graph Attention Temporal Convolutional Networks", Neurocomputing, 410, 387–393, 2020.
  • Li, S., Farha, Y. A., Liu, Y., Cheng, M.-M. and Gall, J.,"MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation", IEEE Trans. Patt. Anal. Mach. Intel., Early Access, 1–13, 2020.
  • Kok, C., Jahmunah, V., Oh, S. L., Zhou, X., Gururajan, R., Tao, X., Cheong, K. H., Gururajan, R., Molinari, F. and Acharya, U. R., "Automated Prediction of Sepsis Using Temporal Convolutional Network", Comp. Bio. Med., 1–10, 2020.
  • Yan, J., Mu, L., Wang, L., Ranjan, R. and Zomaya, A. Y., "Temporal Convolutional Networks For The Advance Prediction Of ENSO", Scientific Reports (Nature Publisher Group), 10, 1–15, 2020.
  • Hochreiter, S. and Schmidhuber, J., "Long Short-Term Memory", Neural Computation, 9(8), 1735–1780, 1997.
  • Gers, F. A., Schmidhuber, J., Cummins, F., "Learning to Forget: Continual Prediction with LSTM", Neural Computation, 12(10), 2451–2471, 2000.
  • Chung, J., Gulcehre, C., Cho, K., Bengio, Y., "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling", arXiv preprint, arXiv:1412.3555, 1–9, 2014.
  • Cho, K., Van Merriënboer, B., Bahdanau, D. and Bengio, Y., "On the Properties of Neural Machine Translation: Encoder-Decoder Approaches", arXiv preprint, arXiv:1409.1259, 2014.
  • Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. and Kavukcuoglu, K., "Wavenet: A Generative Model for Raw Audio", arXiv preprint, arXiv:1609.03499, 1–15, 2016.
  • Hewage, P., Behera, A., Trovati, M., Pereira, E., Ghahremani, M., Palmieri, F. and Liu, Y., "Temporal Convolutional Neural (TCN) Network for an Effective Weather Forecasting Using Time-Series Data from the Local Weather Station", Methodologies and Application, 24, 16453–16482, 2020.
  • Zhen, X., Chakraborty, R., Vogt, N., Bendlin, B. B. and Singh, V., "Dilated Convolutional Neural Networks For Sequential Manifold-valued Data", Proceedings of the IEEE/CVF International Conference on Computer Vision, 10621–10631, 2019.
  • European Centre for Disease Prevention and Control, Geographic Distribution of Covid-19 Cases, Retrieved from: https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide 08/12/2020.
  • Kingma, D. P. and Ba, J. L., "Adam: A Method For Stochastic Optimization", arXiv preprint, arXiv:1412.6980, 2014.
There are 35 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Journals
Authors

Osman Tayfun Bişkin 0000-0002-2326-9438

Publication Date June 29, 2021
Published in Issue Year 2021 Volume: 7 Issue: 1

Cite

APA Bişkin, O. T. (2021). MULTI-STEP FORECASTING OF COVID-19 CASES IN EUROPEAN COUNTRIES USING TEMPORAL CONVOLUTIONAL NETWORKS. Mugla Journal of Science and Technology, 7(1), 117-126. https://doi.org/10.22531/muglajsci.875414
AMA Bişkin OT. MULTI-STEP FORECASTING OF COVID-19 CASES IN EUROPEAN COUNTRIES USING TEMPORAL CONVOLUTIONAL NETWORKS. Mugla Journal of Science and Technology. June 2021;7(1):117-126. doi:10.22531/muglajsci.875414
Chicago Bişkin, Osman Tayfun. “MULTI-STEP FORECASTING OF COVID-19 CASES IN EUROPEAN COUNTRIES USING TEMPORAL CONVOLUTIONAL NETWORKS”. Mugla Journal of Science and Technology 7, no. 1 (June 2021): 117-26. https://doi.org/10.22531/muglajsci.875414.
EndNote Bişkin OT (June 1, 2021) MULTI-STEP FORECASTING OF COVID-19 CASES IN EUROPEAN COUNTRIES USING TEMPORAL CONVOLUTIONAL NETWORKS. Mugla Journal of Science and Technology 7 1 117–126.
IEEE O. T. Bişkin, “MULTI-STEP FORECASTING OF COVID-19 CASES IN EUROPEAN COUNTRIES USING TEMPORAL CONVOLUTIONAL NETWORKS”, Mugla Journal of Science and Technology, vol. 7, no. 1, pp. 117–126, 2021, doi: 10.22531/muglajsci.875414.
ISNAD Bişkin, Osman Tayfun. “MULTI-STEP FORECASTING OF COVID-19 CASES IN EUROPEAN COUNTRIES USING TEMPORAL CONVOLUTIONAL NETWORKS”. Mugla Journal of Science and Technology 7/1 (June 2021), 117-126. https://doi.org/10.22531/muglajsci.875414.
JAMA Bişkin OT. MULTI-STEP FORECASTING OF COVID-19 CASES IN EUROPEAN COUNTRIES USING TEMPORAL CONVOLUTIONAL NETWORKS. Mugla Journal of Science and Technology. 2021;7:117–126.
MLA Bişkin, Osman Tayfun. “MULTI-STEP FORECASTING OF COVID-19 CASES IN EUROPEAN COUNTRIES USING TEMPORAL CONVOLUTIONAL NETWORKS”. Mugla Journal of Science and Technology, vol. 7, no. 1, 2021, pp. 117-26, doi:10.22531/muglajsci.875414.
Vancouver Bişkin OT. MULTI-STEP FORECASTING OF COVID-19 CASES IN EUROPEAN COUNTRIES USING TEMPORAL CONVOLUTIONAL NETWORKS. Mugla Journal of Science and Technology. 2021;7(1):117-26.
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