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Supervisors:
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Academic degree: doctoral degree in Economics (2005) and Engineering (2015).
Current position at TSI: professor (since 2019), pro-dean of the Engineering Faculty (since 2021), researcher and head of Data Analytics and Artificial Intelligence research cluster at Transport and Telecommunication Institute, TSI (since 2020).
Experience: academic career from an assistant position at Saratov State University, Russia, in 2002 to an associate professor at TSI. Chair of TSI Mathematical Methods and Modelling Department (2014-2020).
Academic experience: Nineteen years of teaching experience in higher education, development and presenting study courses: Probability Theory and Mathematical Statistics; Econometrics; Operation Research; Optimisation Theory; Discrete Mathematics; Introduction to Stochastic Processes; Data Analysis and Business Forecasting. Member of TSI final attestation commissions on MSc in Computer Science and Management of Information Systems. Dr Dmitry Pavlyuk has supervised 15 successfully promoted MSc and 10 BSc theses. Teaching mobility to Higher School of Transport, Bulgaria (2013), Vilnius Gediminas Technical University, Lithuania (2017).
Research experience: Author of more than 50 publications, including 22 journal articles in Sensors (ISSN: 1424-8220), European Transport Research Review (ISSN: 1867-0717), Algorithms (ISSN: 1999-4893), Transport and Telecommunication (ISSN: 1407-6179), Research in Transportation Economics (ISSN: 0739-8859), Transport (ISSN: 1648-4142) and 12 book chapters in Springer’s Lecture Notes in Networks and Advances in Intelligent Systems and Computing book series. Postdoctoral researcher at TSI (2017-2020) for spatiotemporal urban traffic modelling; presenter at more than 30 international conferences in Latvia, Austria, Cyprus, France, Greece, Germany, Poland, Russia, Spain. The reviewer at many top scientific journals, including Transportation Research, Parts A, C, E, Transport Reviews, IEEE Access, Transportmetrica B. Guest editor at Information (ISSN: 2078-2489); member of programme committees of Reliability and Statistics in Transportation and Communication (RelStat-2017-2020), Latvia, and Computer Modelling in Decision Making (CMDM 2017-2020), Russia. Member of PhD promotion committee, expert of the Latvian Council of Science (since 2017) in Civil Engineering.
Participation in research projects: Dr Pavlyuk has successfully completed the postdoc research project 1.1.1.2/VIAA/1/16/112 “Spatiotemporal urban traffic modelling using big data” (2017-2020); researcher in “Enhancing excellence and innovation capacity in sustainable transport interchanges” (Horizon 2020 Nr. 692426, 2016-2018), “Learning with ICT use” (Erasmus+, 2014-2017) and several commercial research projects.
Research Interests: Research interests include: spatial and spatiotemporal statistical modelling, multivariate time series analysis, econometric models and statistical estimators, stochastic frontier models and machine learning in applied domains of banking, airport industry, urban road traffic, public transport, venture markets, and environmental management.
Project Type: Commercial
Main Challenge:
Cash flow forecasting is the process of estimating the future cash inflows that is crucial for any businesses . MSC cash flows are highly complex and interconnected with other financial indicators, thus their forecasting and requires intensive use of powerful time series forecasting models like ARIMA, VAR, and VECM and hybrid frameworks like Neural Prophet.
One of the main project challenges is the need for the model we built to be highly flexible and easily adaptable for cash flow forecasting across different MSC branches.
The project’s successful delivery and integration into live operations were made possible through the synergy between the MSC Shared Service Center Riga in-house team’s proficiency in BI solutions and the TSI DA&AI research cluster’s expertise in advanced time series forecasting.
Customer: MSC Shared Service Center Riga
DA&AI tools:
Multivariate Time Series forecasting:
Machine Learning:
Acknowledgement:
For us at MSC Shared Service Center Riga this was a new and valuable experience both in Machine Learning and collaboration with universities on a Research and Development project. From the Machine Learning point of view the main outcome was the actual viability of modern technologies in a daily process with real data. Collaboration with the university has proven that you don’t need to have in-house professionals for project implementation, you need to have a business case with data, knowledge and expertise can be provided by university experts. I would say this was a win-win situation for both sides.
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