This paper investigates the application of neural basis expansion analysis with exogenous variables (NBEATSx) in the prediction of daily stock realized volatility for various time steps. It compares NBEATSx’s forecasting accuracy and robustness with several commonly used models, namely Long-Short Term …

This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric financial turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and …

Information can be exchanged in many different ways, the standard format for textual information is a document, which comes in an impossible number of different structures. The Ministry of Internal Affairs and Kingdom Relations concerns an overwhelming number of documents on a daily basis and uses a …

Machine learning models have proven to be reliable methods in classification tasks. However, little research has been done on classifying dwelling characteristics based on smart meter & weather data before. Gaining insights into dwelling characteristics can be helpful to create/improve the policies for …

Tijdens het onderzoek is een oplossing gezocht waarmee tekst geclassificeerd kan worden. Dit met het doel informatie uit teksten te halen. Hiervoor is gezocht naar een machine learning oplossing binnen het vakgebied van Natural Language Processing (NLP). Deelvraag 1 Voor de eerste deelvraag is deskresearch …