Références bibliographiques
Voici quelques références pertinentes permettant d’aller plus loin dans les concepts mathématiques vus dans cette formation.
Batra, N., Spina, A., Blomquist, P., & Campbell, F. (2021). Time Series and Outbreak Detection. In N. Batra (Ed.), The epidemiologist R handbook. Applied Epi. https://epirhandbook.com/en/new_pages/time_series.html.
Berkat, R. (2023). Modélisation spatio-temporelle de l’évolution de la dengue en lien avec des prédicteurs environnementaux dans le département de Méta en Colombie entre 2011 et 2019. École de santé publique de l’Université de Montréal. http://hdl.handle.net/1866/28180.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3):307-327.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2016). Time Series Analysis: Forecasting and control. Wiley Series in Probability and Statistics. John Wiley & Sons, Inc., Hoboken, NJ, fifth edition.
Brockwell, P. J. and Davis, R. A. (2016). Introduction to Time Series and Forecasting. Springer Texts in Statistics. Springer, third edition.
Cairo, A. (2012). The Functional Art: An introduction to information graphics and visualization. New Riders.
Dai, J., Xiao, Y., Sheng, Q., Zhou, J., Zhang, Z., & Zhu, F. (2024). Epidemiology and SARIMA model of deaths in a tertiary comprehensive hospital in Hangzhou from 2015 to 2022. BMC Public Health, 24(1), 2549.
Dickey, D. A. and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a):427-431.
Eddy, S. R. (1996). Hidden Markov models. Current Opinion in Structural Biology, 6(3):361-365.
Ekinci, A. (2021). Modelling and forecasting of growth rate of new covid-19 cases in top nine affected countries: Considering conditional variance and asymmetric effect. Chaos, Solitons & Fractals, 151:111227.
Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50:987-1007.
He, Z., & Tao, H. (2018). Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study. International Journal of Infectious Diseases, 74, 61-70.
Herrera, D. (2023, June 20). Forecasting Volatility: Deep Dive into ARCH & GARCH Models. Medium. https://medium.com/@corredaniel1500/forecasting-volatility-deep-dive-into-arch-garch-models-46cd1945872b
Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of statistical software, 27, 1-22.
Kwiatkowski, D., Phillips, P. C., Schmidt, P., and Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3):159-178.
Li, H., & Lu, Y. (2017). Coherent forecasting of mortality rates: A sparse vector-autoregression approach. ASTIN Bulletin: The Journal of the IAA, 47(2), 563-600.
Monigatti, L. (2023). Stationarity in Time Series — A Comprehensive Guide. https://towardsdatascience.com/stationarity-in-time-series-a-comprehensive-guide-8beabe20d68.
Nasri, B. R. (2022). Tests of serial dependence for multivariate time series with arbitrary distributions. Journal of Multivariate Analysis, 192, 105102.
Nasri, B. R., Rémillard, B. N., & Bahraoui, T. (2022). Change-point problems for multivariate time series using pseudo-observations. Journal of Multivariate Analysis, 187, 104857.
Nasri, B. R., Rémillard, B. N., & Thioub, M. Y. (2024). Are information criteria good enough to choose the right number of regimes in hidden Markov models?. Journal of Statistical Computation and Simulation, 94(18), 4107-4127.
Njongwa Yepnga, N. (2023). 100 Jours De ML : Vidéos 88-92, 97. https://www.youtube.com/playlist?list=PLyh35eYRez8e0KxsArawcDm6TYPVq0rU_.
Shumway, R. H. and Stoffer, D. S. (2017). Time Series Analysis and its Applications: With R examples. Springer Texts in Statistics. Springer, Cham, fourth edition.
Thomson, M. C. and Stanberry, L. R. (2022). Climate change and vectorborne diseases. New England Journal of Medicine, 387(21):1969-1978.
Wang, Z. D., Yang, C. X., Zhang, S. K., Wang, Y. B., Xu, Z., & Feng, Z. J. (2024). Analysis and forecasting of syphilis trends in mainland China based on hybrid time series models. Epidemiology & Infection, 152, e93.
Xavier, L. L., Honório, N. A., Pessanha, J. F. M., and Peiter, P. C. (2021). Analysis of climate factors and dengue incidence in the metropolitan region of Rio de Janeiro, Brazil. PLoS One, 16(5): e0251403. https://doi.org/10.1371/journal.pone.0251403.