Montpellier Business School

Dr. Chen Yi-Ting

Chen Yi-Ting
Fonction Associate professor
Research themes AI and Machine Learning, Big Data, Business and Decision Analytics, Computer Science, Information Technology, Operations Research
Teaching department Operations, information & decisions


Selected intellectual contributions

Chen Y.-T., Sun E., Chang M.-F. & Lin Y.-B. Forthcoming. Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data. Annals of Operations Research. Posedel Šimović P., Chen Y.-T. & Sun E.. 2023. Classifying the variety of customers online engagement for churn prediction with a mixed-penalty logistic regression. Computational Economics, 61: 451-485. Lai W., Chen Y.-T. & Sun E. 2022. Risk Factor Extraction with Quantile Regression Method. Annals of Operations Research, 316(2): 1543-1572 . Chen Y. T., Sun E., Chang M.F. & Lin Y.B. 2021. Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0. International Journal of Production Economics, 238: 108157. Chen Y.T., Sun E.W. & Lin Y.B. 2020. Machine learning with parallel neural networks for analyzing and forecasting electricity demand. Computational Economics. 56(2): 569–597. Lai W., Chen YT & Sun EW (2021). Comonotonicity and low volatility effect. Annals of Operations Research. 299 (1-2): 1057-1099. Chen Y.T., Sun E.W. & Lin Y.B. 2020. Merging anomalous data usage in wireless mobile telecommunications: Business analytics with a strategy-focused data-driven approach for sustainability. European Journal of Operational Research, 281(3): 687-705. Sun E.W., Kruse T. & Chen Y.T. 2019. Stylized algorithmic trading: satisfying the predictive near-term demand of liquidity. Annals of Operations Research. 281(1/2): 315–347. Chen Y.T., Lai W. & Sun E.W. 2019. Jump detection and noise separation by a singular wavelet method for predictive analytics of high-frequency data. Computational Economics. 54(2): 809–844.

Download our Summer School brochure
This field is for validation purposes and should be left unchanged.
Contact us
Preferred contact method(Required)
This field is for validation purposes and should be left unchanged.
Je préfère être contacté par(Required)
This field is for validation purposes and should be left unchanged.