Home > Ārējās vides efektu modelēšana, izmantojot Markova modulētās regresijas

Ārējās vides efektu modelēšana, izmantojot Markova modulētās regresijas

Periods: 01.01.2017

- 01.01.2020

Projekta vadītājs/a:

Asoc. profesors

Dr. sc. ing.

Nadežda Spiridovska

Inženierzinātņu fakultāte

spiridovska.n@tsi.lv

Vairāk

Academic degree and current position in TSI: associate professor, researcher at the Engineering Faculty.

Previous experience: worked at the Mathematical Methods and Modelling Department in TSI for more than 10 years (assistant, lecturer, assistant professor), postdoc researcher.

Membership: A member of the Latvian Simulation Society since 2004, a member of the Latvian Operations Research Society (LatORS) since 2019.

Teaching activity: Discrete Mathematics (undergraduate level); Probability Theory and Mathematical Statistics (undergraduate level); System Modelling (undergraduate level); Introduction to machine learning (undergraduate level); Statistics (undergraduate level).

Publication activity: Author of on average two to three academic articles a year.

Projects: as a researcher has participated in more than 5 European and Latvian research projects.

Supervised Doctoral, Master and Bachelor Theses (number): 1 Master thesis and more than 10 Bachelor theses.

Research fields/domains: Data analysis, statistical analysis, mathematical modelling and simulation (in transportation), machine learning.

Motto: “Any problem can be turned into opportunity”.

Projekta Tips: Metodiskais

Galvenais izaicinājums:

Projekts ir veltīts netradicionālu regresijas modeļu izstrādei, proti, Markovas modulētai lineārajai regresijai, lai analizētu satiksmes plūsmu un blakus esošos transporta uzdevumus un atrastu algoritmus to parametru novērtēšanai lieliem datiem.

Finansējums: ERAF, 1.1.1.2/VIAA/1/16/075

DA&AI rīki:

Statistiskie modeļi
Markova procesi

Rezultāti (publikācijas, ziņojumi, apliecinājumi utt.)

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.

Produkti:

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

Ārējās vides efektu modelēšana, izmantojot Markova modulētās regresijas

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