Difference between revisions of "Official COST FinAI Publications"
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6. Wei, L., Denis, M.B. Day-ahead electricity prices prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling. | 6. Wei, L., Denis, M.B. Day-ahead electricity prices prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling. | ||
− | 7. Escobar, | + | 7. Escobar, D., Paraschiv, F., Schürle, M. Pricing electricity futures with distortion functions under model ambiguity. |
== COST FinAI Publications == | == COST FinAI Publications == | ||
1. [https://drive.google.com/drive/folders/1cthF7YgDmTOePUaLdSsI8wSlzhuHUVV- COST FinAI presentations and executive summaries] | 1. [https://drive.google.com/drive/folders/1cthF7YgDmTOePUaLdSsI8wSlzhuHUVV- COST FinAI presentations and executive summaries] |
Revision as of 14:12, 5 January 2021
Here you find a list of all academic publications that were created in the context of our COST FinAI Action.
Please kindly add the appropriate acknowledgement. For academic articles, that would be:
- This publication is based upon work from COST Action 19130, supported by COST (European Cooperation in Science and Technology), www.cost.eu
Academic peer-reviewed papers
Working papers
1. Devine, M.T, Russo, M., Cuffe, P., Blockchain electricity trading using tokenised power delivery contract.
2. K. Khowaja, D. Saef, S. Sizov, and W. K. Härdle. Data Analytics Driven Controlling: bridging statistical modeling and managerial intuition. IRTG 1792 Discussion Paper 2020-026, 2020.
3. Paraschiv, F., Schmid, M., Wahlstrøm, R. R. Bankruptcy prediction of privately held SMEs using feature selection methods.
4. Wahlstrøm, R. R., Paraschiv, F., Schürle, M. A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Models. Available at SSRN: http://dx.doi.org/10.2139/ssrn.3600955
5. Wei, L., Paraschiv, F. Modelling the Evolution of Wind and Solar Power Infeed Forecasts. Available at SSRN: [1]
6. Wei, L., Denis, M.B. Day-ahead electricity prices prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling.
7. Escobar, D., Paraschiv, F., Schürle, M. Pricing electricity futures with distortion functions under model ambiguity.