Zoekresultaten

Professorship Transformational Media

This paper starts with the explanation of the research rationale of the professorship. Subsequently, an exploration of the research agenda is provided, focusing on the two core research themes of the professorship: transformational content strategy and transformational content design. Within this section, knowledge gaps will be identified and examples will be presented of research projects related to each theme. Finally, light will be shed on the research approach, offering a brief overview of the theoretical approach, research methodology, and expected impact.

Inclusive education

Inclusief onderwijs staat hoog op de agenda van De Haagse Hogeschool. Sinds januari 2021 is Naomi van Stapele lector Inclusive Education bij het kenniscentrum Global & Inclusive Learning. In deze intreerede van september 2022 wordt o.a. ingegaan op onzekerheid, de drie leidende beginselen van inclusief onderwijs, de ethische politiek van inclusiviteit, etc.

Smart grid Lucas Nuelle

Rapport van S3 studenten die aan de Lucas Nuelle panelen hebben gewerkt met de Smart Grid module

An offshore energy simulation through flow networks: CEL within the MSP Challenge 2050 simulation game platform

This paper presents the design of the offshore energy simulation CEL as a flow network, and its integration in the MSP Challenge 2050 simulation game platform. This platform is designed to aid learning about the key characteristics and complexity of marine or maritime spatial planning (MSP). The addition of CEL to this platform greatly AIDS MSP authorities in learning about and planning for offshore energy production, a highly topical and big development in human activities at sea. Rather than a standard flow network, CEL incorporates three additions to accommodate for the specificities of energy grids: an additional node for each team's expected energy, a split of each node representing an object into input and output parts to include the node's capacity, and bidirectional edges for all cables to enable more complex energy grid designs. Implemented with Dinic's algorithm it takes less than 30ms for the simulation to run for the average amount of grids included in an MSP Challenge 2050 game session. In this manner CEL enables MSP authorities and their energy stakeholders to use MSP Challenge 2050 for designing and testing more comprehensive offshore energy grids.

Lessons learned bij projecten voor gebiedsgerichte inzameling van bedrijfsafval in Amsterdam

Can transformers transform financial forecasting?

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 of these models in handling temporal information in financial time series. Design/methodology/approach Employing a robust methodological framework, the study systematically compares a range of Transformer models, including first-generation and advanced iterations like Informer, Autoformer, and PatchTST, against benchmark models (HAR, NBEATSx, NHITS, and TimesNet). The evaluation encompasses 80 different stocks, four error metrics, four statistical tests, and three robustness tests designed to reflect diverse market conditions and data availability scenarios. Findings The research uncovers that while first-generation Transformer models, like TFT, underperform in financial forecasting, second-generation models like Informer, Autoformer, and PatchTST demonstrate remarkable efficacy, especially in scenarios characterized by limited historical data and market volatility. The study also highlights the nuanced performance of these models across different forecasting horizons and error metrics, showcasing their potential as robust tools in financial forecasting, which contradicts the findings of Zeng et al. (2023) Originality/value This paper contributes to the financial forecasting literature by providing a comprehensive analysis of the applicability of Transformer-based models in this domain. It offers new insights into the capabilities of these models, especially their adaptability to different market conditions and forecasting requirements, challenging the existing skepticism created by Zeng et al. (2023) about their utility in financial forecasting.