This paper presents a user-friendly version of Persistent Homology (PH) graph code to model financial market structures and changes. By leveraging Topological Data Analysis (TDA), the code offers an effective approach for analyzing high-dimensional stock data, enabling the identification of persistent …

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 …

Objective: To construct the underlying value structure of shared decision making (SDM) models. Method: We included previously identified SDM models (n = 40) and 15 additional ones. Using a thematic analysis, we coded the data using Schwartz’s value theory to define values in SDM and to investigate value …

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 …

City governments increasingly experiment with civic participation in the procurement and the realization of smart city technologies in order to improve the incorporation of human values. In this paper, a model is proposed with the level of participation, the continuity of participation and the extent …

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 …

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Purpose : Adolescents are insufficiently physically active and spent too much time sitting, which provides a health risk. In the current study the municipality of Amsterdam, secondary schools, experts in urban sports and researchers work together to stimulate healthy exercise behavior of adolescents. …

This study of Windesheim University, lectorate Educational innovations and ICT, explored how, under pressure from the COVID-19 measures taken to avoid physical contact, an online learning environment for part-time bachelor’s degree students was realized and to what extent this online learning environment …