Sökning: "Vector Autoregressive Models"

Visar resultat 1 - 5 av 57 uppsatser innehållade orden Vector Autoregressive Models.

1. 1. Fractional Cointegration and Price Discovery in FX Markets

   D-uppsats, Handelshögskolan i Stockholm/Institutionen för nationalekonomi

   Författare :Johan Faxner; [2023]
   Nyckelord :exchange rates; price discovery; fractional cointegration; market microstructure; covered interest rate parity;

   Sammanfattning : I employ bivariate fractionally cointegrated vector autoregressive models to analyze price discovery on the EUR/GBP market. Using daily spot rates between 2010 and 2022 along with corresponding one-month and three-month forward rates, I extract parameter estimates for pairwise long-run relationships, each pair containing a spot and a forward. LÄS MER

3. 2. Algorithmic Approaches to Output Prediction in a Virtual Power Plant

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Johannes Rosing; Oscar Ekhed; [2023]
   Nyckelord :Autoregressive Models; Deep Learning; Machine Learning; Power Output; Random Forest Regression; Strategic Recommendations; Support Vector Regression; Virtual Power Plant;

   Sammanfattning : Virtual Power Plants (VPPs) are an emerging form of technology that allows owners of electricity producing appliances, such as electric vehicles, to partake in a pool of producers of sustainable energy. The Swedish electricity grid owner Svenska Kraftnät hosts a platform where VPPs act as intermediaries between energy producing customers and third party buyers. LÄS MER

4. 3. Forecasting copper price using VAR and the XGBoost model: an experiment with a relatively small dataset

   Magister-uppsats, Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionen

   Författare :Juanli Hu; [2023]
   Nyckelord :copper price; Vector autoregressive model; XGBoost; Time series; Business and Economics;

   Sammanfattning : Given the importance of copper prices to investors, governments, and policymakers, this paper investigates short-term price predictability using VAR and XGBoost models. All models are trained with historical data from November 2021 to December 2022 and using MSE, RMSE and MAE for evaluating the model performance. LÄS MER

5. 4. Predicting Waveforms with Machine Learning for Efficient Triggering in Monitoring Systems

   Master-uppsats, Mälardalens universitet/Akademin för innovation, design och teknik

   Författare :Amanda Rautio; [2023]
   Nyckelord :;

   Sammanfattning : Energy systems need to evolve to meet the requirements of the modern world and the future. Hence, substantial effort is needed at an academic and industrial level to develop valuable diagnostic techniques. LÄS MER

7. 5. Towards predictive modelling of solar power productionHandledare: Hadi BanaeeExaminer: Andrey Kiselev© Hadi

   Uppsats för yrkesexamina på grundnivå, Örebro universitet/Institutionen för naturvetenskap och teknik

   Författare :Hadi Ilani; [2022]
   Nyckelord :solar power production; Machine learning; Predictive models; solenergiproduktion; maskininlärning; prediktiv modell;

   Sammanfattning : In 2019, 732 solar panels were installed on the roof of a building at Örebro University. Thesolar power production of the facility has been collected in a database in Akademiska Hus,along with several parameters from a weather station in the same building. LÄS MER