• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Monthly Data Prediction with Bayesian Hierarchical Models

Student: Sapelnikova Maria

Supervisor: Boris Demeshev

Faculty: Faculty of Economic Sciences

Educational Programme: Economics (Bachelor)

Year of Graduation: 2020

The paper considers three groups of models for constructing forecasts of monthly time series: econometric, machine learning models and based on the Bayesian approach. Each class of models is represented by several models that are most applicable for predicting monthly data. In addition, for each model, a single-level representation is used, as well as a multi-level one. The ultimate goal of the study is a comparative analysis of the quality of predictions of each class of models, as a result of which a conclusion is drawn about the best model for predicting selected data in accordance with their key characteristics. Based on the results of the comparison, neural networks showed the best quality on two forecasting horizons, however, in the case of short time series, the hierarchical Bayesian model showed nearly similar results

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses