Abstract
Since the outbreak of coronavirus disease 2019 (COVID-19) has resulted in a dramatic loss of human life and economic disruption worldwide from early 2020, numerous studies focusing on COVID-19 forecasting were presented to yield accurate predicting results. However, most existing methods could not provide satisfying forecasting performance due to tons of assumptions, poor capability to learn appropriate parameters, etc. Therefore, in this paper, we combine a traditional time series decomposition: local mean decomposition (LMD) with temporal convolutional network (TCN) as a general framework to overcome these shortcomings. Based on the particular architecture, it can solve weekly new confirmed cases forecasting problem perfectly. Extensive experiments show that the proposed model significantly outperforms lots of state-of-the-art forecasting methods, and achieves desirable performance in terms of root mean squared log error (RMSLE), mean absolute percentage error (MAPE), Pearson correlation (PCORR), and coefficient of determination (\(R^2\)). To be specific, it could reach 0.9739, 0.8908, and 0.7461 on \(R^2\) when horizon is 1, 2, and 3 respectively, which proves the effectiveness and robustness of our LMD-TCN model.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
- 3.
The source code of our method will be available after this paper is published.
References
Al-Qaness, M.A., Ewees, A.A., Fan, H., Abd El Aziz, M.: Optimization method for forecasting confirmed cases of covid-19 in china. J. Clin. Med. 9(3), 674 (2020)
Alabdulrazzaq, H., Alenezi, M.N., Rawajfih, Y., Alghannam, B.A., Al-Hassan, A.A., Al-Anzi, F.S.: On the accuracy of arima based prediction of covid-19 spread. Results Phys. 27, 104509 (2021)
Almeida, R.: Analysis of a fractional seir model with treatment. Appl. Math. Lett. 84, 56–62 (2018)
Anastassopoulou, C., Russo, L., Tsakris, A., Siettos, C.: Data-based analysis, modelling and forecasting of the covid-19 outbreak. PLoS ONE 15(3), e0230405 (2020)
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)
Behnood, A., Golafshani, E.M., Hosseini, S.M.: Determinants of the infection rate of the covid-19 in the us using anfis and virus optimization algorithm (voa). Chaos, Solitons Fractals 139, 110051 (2020)
Benesty, J., Chen, J., Huang, Y., Cohen, I.: Pearson correlation coefficient. In: Noise reduction in speech processing, pp. 1–4. Springer (2009)
Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time series analysis: forecasting and control. John Wiley & Sons (2015)
Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Chimmula, V.K.R., Zhang, L.: Time series forecasting of covid-19 transmission in Canada using lstm networks. Chaos, Solitons Fractals 135, 109864 (2020)
Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)
Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990)
Harko, T., Lobo, F.S., Mak, M.: Exact analytical solutions of the susceptible-infected-recovered (sir) epidemic model and of the sir model with equal death and birth rates. Appl. Math. Comput. 236, 184–194 (2014)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Huang, N.E., et al.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. London. Series A: mathematical, physical and engineering sciences 454(1971), 903–995 (1998)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456. PMLR (2015)
Li, S., et al.: Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. In: Advances in Neural Information Processing Systems 32 (2019)
Makridakis, S., Hibon, M.: The m3-competition: results, conclusions and implications. Int. J. Forecast. 16(4), 451–476 (2000)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: ICML (2010)
Rahman, M.S., Chowdhury, A.H., Amrin, M.: Accuracy comparison of arima and xgboost forecasting models in predicting the incidence of covid-19 in bangladesh. PLOS Global Public Health 2(5), e0000495 (2022)
Rosenblatt, F.: The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory (1957)
Shaban, W.M., Rabie, A.H., Saleh, A.I., Abo-Elsoud, M.: A new covid-19 patients detection strategy (cpds) based on hybrid feature selection and enhanced knn classifier. Knowl.-Based Syst. 205, 106270 (2020)
Shoeibi, A., et al.: Automated detection and forecasting of covid-19 using deep learning techniques: a review. arXiv preprint arXiv:2007.10785 (2020)
Singh, V., Poonia, R.C., Kumar, S., Dass, P., Agarwal, P., Bhatnagar, V., Raja, L.: Prediction of covid-19 corona virus pandemic based on time series data using support vector machine. J. Discrete Math. Sci. Cryptography 23(8), 1583–1597 (2020)
Smith, J.S.: The local mean decomposition and its application to eeg perception data. J. R. Soc. Interface 2(5), 443–454 (2005)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Vaswani, A., et al.: Attention is all you need. Advances in Neural Information Processing Systems 30 (2017)
Wang, L., Adiga, A., Venkatramanan, S., Chen, J., Lewis, B., Marathe, M.: Examining deep learning models with multiple data sources for covid-19 forecasting. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 3846–3855. IEEE (2020)
Wibbens, P.D., Koo, W.W.Y., McGahan, A.M.: Which covid policies are most effective? a bayesian analysis of covid-19 by jurisdiction. PLoS ONE 15(12), e0244177 (2020)
Zeroual, A., Harrou, F., Dairi, A., Sun, Y.: Deep learning methods for forecasting covid-19 time-series data: a comparative study. Chaos Solitons Fractals 140, 110121 (2020)
Zhao, X., Barber, S., Taylor, C.C., Nie, X., Shen, W.: Spatio-temporal forecasting using wavelet transform-based decision trees with application to air quality and covid-19 forecasting. J. Appl. Stat., 1–19 (2022)
Chandra, R., Jain, A., Singh Chauhan, D.: Deep learning via lstm models for covid-19 infection forecasting in India. PLoS ONE 17(1), e0262708 (2022)
Kumar, S., Sharma, R., Tsunoda, T., Kumarevel, T., Sharma, A.: Forecasting the spread of covid-19 using lstm network. BMC Bioinform. 22(6), 1–9 (2021)
Acknowledgements
This work was supported by the Natural Science Foundation of Guangdong Province, China (2020A1515010761).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sun, L., Liu, Z., Zhan, C., Min, H. (2022). COVID-19 Forecasting Based on Local Mean Decomposition and Temporal Convolutional Network. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13629. Springer, Cham. https://doi.org/10.1007/978-3-031-20862-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-20862-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20861-4
Online ISBN: 978-3-031-20862-1
eBook Packages: Computer ScienceComputer Science (R0)