Essays on Time Series Forecasting and Machine Learning
Year: 2025
Type: dissertation
Abstract: This dissertation investigates how modern machine learning (ML) methods, with a particular focus on multi-task learning (MTL) frameworks, can enhance forecasts in financial and macroeconomic contexts. Chapter 1 outlines the limitations of traditional econometric approaches in environments characterized by nonlinearity, structural breaks, and mixed-frequency data, and motivates the use of ML models capable of exploiting shared information across related prediction tasks. Chapter 2 evaluates the performance of temporal fusion transformers (TFT) for forecasting realized volatility (RV) of US stocks. It shows that models with sector-level pooling significantly outperform asset-specific or fully pooled models, and that TFT surpasses traditional volatility models and common ML benchmarks, especially in volatile market phases. Chapter 3 extends this approach by introducing a two-dimensional MTL framework to jointly forecast RV across industry portfolios and forecast horizons. Using long short-term memory (LSTM) models with structured parameter sharing, the study finds that sharing information across both dimensions improves forecast accuracy, particularly at short horizons. Chapter 4 applies MTL to macroeconomic nowcasting using disaggregated US labor market and inflation data. Results show that MTL enhances nowcast accuracy compared to single-task and traditional models, with the optimal degree of information sharing depending on task-specific heterogeneity. Chapter 5 embeds MTL into a mixed frequency data sampling (MIDAS) setting by combining it with different MIDAS techniques to nowcast sectoral employment using high-frequency indicators. Nonlinear MTL models, particularly those based on extreme gradient boosting (XGBoost), outperform benchmarks and highlight the value of preserving intra-month dynamics. Overall, the dissertation demonstrates that combining MTL with flexible ML architectures offers a powerful framework for improving forecast quality and stability in complex economic settings.
Author: Frank, Johannes
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7109568429