Purpose This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations …

This study proposes a novel loss function for neural network models that explores the topological structure of stock realized volatility (RV) data by adding Wasserstein Distance (WD). The study shows that the proposed loss statistically significantly improves the forecast accuracy of neural network models …

This paper presents user-friendly code for the implementation of a loss function for neural network time series models that exploits the topological structures of financial data. By leveraging the recently-discovered presence of topological features present in financial time series data, the code offers …

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 proposes a novel theory, coined as Topological Tail Dependence Theory, that links the mathematical theory behind Persistent Homology (PH) and the financial stock market theory. This study also proposes a novel algorithm to measure topological stock market changes as well as the incorporation …

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 …

-

Forecasting long-term consequences of global warming requires knowledge on thermal mortality and how heat stress interacts with other environmental stressors on different timescales. Here, we describe a flexible analytical framework to forecast mortality risks by combining laboratory measurements on …

This paper presents data-driven insights from a case study that was carried out in an University EV charging plaza where EV charging demand is met with the combination of the University campus grid and installed solar capacity. First, we assessed the plaza dependency on the grid for meeting EV charging …

Here, we delve into Demand Forecasting via Machine Learning, dissecting how to predict future demand using time-sensitive data. Westveer highlights key forecasting models, from the basic Simple Exponential Smoothing to the advanced SARIMA, applied to an electricity production dataset. The session, encapsulating …