Modelling External Environment Effects using Markov-modulated regressions

Project Type: Academic

Main Challenge:

The project is devoted to developing of non-traditional regression models, namely Markov-modulated linear regression for analysis of traffic flow and adjacent transport tasks and find algorithms for their parameter estimation for big data.

Funding: ERDF, 1.1.1.2/VIAA/1/16/075

DA&AI tools:

• Statistical models
• Markov processes

Deliverables (publications, reports, acknowledgments, etc.)

• Spiridovska. “Markov-Modulated Processes, Their Applications and Big Data Cases: State of the Art” In book: “Reliability and Statistics in Transportation and Communication. RelStat 2019”. I. Kabashkin, I. Yatskiv and O. Prentkovskis eds. Springer, Cham. 2020. pp. 100-109.
• Yatskiv and N. Spiridovska. “Data Preparation Framework Development for Markov-Modulated Linear Regression Analysis” In book: “Reliability and Statistics in Transportation and Communication. RelStat 2018”. I. Kabashkin, I. Yatskiv and O. Prentkovskis eds. Springer, Cham. 2019. pp. 178-190.
• Spiridovska. “Markov-Modulated Linear Regression: Tasks and Challenges in Transport Modelling”. Reliability and Statistics in Transportation and Communication. I. Kabashkin, I. Yatskiv and O. Prentkovskis eds. 2018. pp. 223-231.
• Spiridovska and I. Yatskiv. “Public transport passenger flow analysis and prediction using alternating Markov-modulated linear regression”. In 29th European Conference on Operational Research (Euro2018) handbook. 2018. pp. 208.
• Spiridovska. “A Quasi-Alternating Markov-Modulated Linear Regression: Model Implementation Using Data about Coaches’ Delay Time”, international journal of circuits, systems and signal processing, Vol. 12. 2018, pp. 617-628.
• Spiridovska. “Markov-Modulated Linear Regression: a Case Study of Coaches’ Delay Time”, International Journal of Economics and Management Systems, Vol. 3. 2018, pp. 53-59.
• Spiridovska. “Markov-Modulated Linear Regression: Tasks and Challenges in Transport Modelling” In book: “Reliability and Statistics in Transportation and Communication”. I. Kabashkin, I. Yatskiv and O. Prentkovskis eds. Springer, Cham. 2018. pp. 223-231.I. Kabashkin and I. Yatskiv eds. 2017. pp. 46.

Products:

• MMLR: Fitting Markov-Modulated Linear Regression Models, CRAN R package, https://cran.r-project.org/web/packages/MMLR