Project 1: Predicting weekly number of battle deaths in civil wars (Gudmund Hermansen - gudmunhh@math.uio.no). In this project you will look at some competing statistical methods for modelling the weekly number of battle deaths in a civil war. We will work with conflict data from the https://ucdp.uu.se/ (a comprehensive database on conflict data) and investigate battle deaths time series for several countries. The main focus of the project is to predict the number of battle deaths in the following week, month, etc. and the techniques for evaluating and comparing predicting performance of competing methods. Note that no prior knowledge of time series analysis is required, and a significant part of the project will be about practical data analysis and exploration.
Project 2: Count time series modelling of financial data (Gudmund Hermansen - gudmunhh@math.uio.no). In finance, there are several types of discrete data series (i.e. time series where what we observe is an integer value), for example the number of transactions of a stock or number of trades in a currency in foreign exchange on a given market place, or the number new of quotes added or cancelled in a given order book (on a electronic market place) within a fixed time period (millisecond, second, minute, etc. ). In this project, you will work with such data series from high frequency foreign exchange and investigate how methods from so-called count time series literature can be used to model the underlying dynamics of such data. Note that no prior knowledge of time series analysis is required. The first part of this project will be to familiarise you with such methodology, the second part will focus on data, analysis and inference.
Project 3: Machine learning and high frequency financial time series (Gudmund Hermansen - gudmunhh@math.uio.no). In this project you will compare more traditional statistical models developed for high frequency financial time series with competing methods from machine learning. You will work with several examples of high frequency tick data from foreign exchange, and explore possibilities and limitations of both approaches.
Project 4: Principal component analysis (PCA) for high frequency data (Gudmund Hermansen - gudmunhh@math.uio.no). This project is based on the work in http://galton.uchicago.edu/~mykland/paperlinks/PCA_v20180204.pdf. The first part of the project will be to read, understand and summarise the paper. The second part of the project focuses on implementation of the PCA method on high frequency foreign exchange data.
Project 5: Applied data analysis and statistical modelling for a kaggle-like competition or dataset. (Gudmund Hermansen - gudmunhh@math.uio.no). Within this project applied data analysis and predictive modelling will be carried out. A student is allowed to choose a competition or a data-set of interest for him/her on one of the popular data science platforms: https://www.kaggle.com/, https://www.topcoder.com/thrive/tracks?track=Data%20Science or https://archive.ics.uci.edu/ml/datasets.php. Then preliminary data analysis should be performed, followed by careful statistical modelling, inference and eventually evaluation of predictions and explaining the results.
Project 6: Climate change and rain (Thordis Thorarinsdottir & Gudmund Hermansen - gudmunhh@math.uio.no). According to climate projections, Norway will become warmer and wetter over the course of the century. There will be more precipitation overall, and events with heavy rainfall will increase in magnitude and occur more frequently. In this project, we will investigate to which extent such changes can already be observed in Oslo.
We will use statistical methods to analyse over 50 years of hourly and daily observed precipitation data from Oslo Blindern. We will investigate various aspects of precipitation patterns related to both how often it rains and how much it rains each time, and search for changes in each of these patterns.
Project 7: Analysis of genetic data (Geir Storvik - geirs@math.uio.no). There are a lot of freely available datasets relating genetic data to different outcomes (diseases, expressions etc). In this project, the aim will be to download one of these datasets and apply some of the methods you have learned in introductory courses as well as looking at some more advanced methods. The main aim will be to evaluate strengths and weaknesses with the chosen methods for the particular application.
Project 8: Stochastic analysis and finance and insurance and risk (Frank Proske - proske@math.uio.no). Students that are interested in a project within stochastic analysis, finance or insurance and risk should contact Gudmund Hermansen (gudmunhh@math.uio.no) or Frank Proske directly for more information.
Project 9: Generative models for imbalances data (Ingrid Hob?k Haff -ingrihaf@math.uio.no). Generative models for imbalances data project description.
Project 10: Simulation of Particles in the Solar Atmosphere (Geir Storvik & Gudmund Hermansen - geirs@math.uio.no). Simulation of particles in the solar atmosphere project description (in Norwegian).