Forecasting Adverse Events

We are exploring statistical and machine learning methods for forecasting events with significant societal implications using time series data.

• Zhao, Z. & Yau, Y. C. (2021). Alternating Pruned Dynamic Programming for Multiple Epidemic Change-point Estimation. Journal of Computational and Graphical Statistics, forthcoming, https://arxiv.org/abs/1907.06810

• Jiang, F., Zhao, Z., & Shao, X. (2021). Time Series Analysis of COVID-19 Infection Curve: A Change-point Perspective. Journal of Econometrics, forthcoming.

• Ahmad, F., Abbasi, A., Kitchens, B., Adjeroh, D. A., & Zeng, D. (2022). Deep Learning for Adverse Event Detection from Web Search. IEEE Transactions on Knowledge and Data Engineering, 34(6),2681-2695.

• Abbasi, A., Li, J., Adjeroh, D. A., Abate, M., & Zheng, W. (2019). Don’t Mention It? Analyzing User-Generated Content Signals for Early Adverse Event Warnings. Information Systems Research, 30(3), 1007-1028.

• Speakman, S., Somanchi, S., McFowland III, E. & Neill, D. B. (2016). Penalized Fast Subset Scanning. Journal of Computational and Graphical Statistics, 25(2),382-404.