Mitic, Peter;
(2024)
Reputation, Sentiment, Time Series and Prediction.
In:
Proceedings of the 13th International Conference on Data Science, Technology and Applications.
(pp. pp. 51-61).
SCITEPRESS - Science and Technology Publications: Dijon, France.
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Abstract
A formal formulation for reputation is presented as a time series of daily sentiment assessments. Projections of reputation time series are made using three methods that replicate the distributional and auto-correlation properties of the data: ARIMA, a Copula fit, and Cholesky decomposition. Each projection is tested for goodnessof-fit with respect to observed data using a bespoke auto-correlation test. Numerical results show that Cholesky decomposition provides optimal goodness-of-fit success, but overestimates the projection volatility. Expressing reputation as a time series and deriving predictions from them has significant advantages in corporate risk control and decision making.
| Type: | Proceedings paper |
|---|---|
| Title: | Reputation, Sentiment, Time Series and Prediction |
| Event: | 13th International Conference on Data Science, Technology and Applications |
| Location: | Dijon, France |
| Dates: | 9th-11th July 2024 |
| ISBN-13: | 978-989-758-707-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.5220/0012762600003756 |
| Publisher version: | http://dx.doi.org/10.5220/0012762600003756 |
| Language: | English |
| Additional information: | © The Author 2024. Original content in this paper is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keywords: | Reputation, Sentiment, Time Series, Prediction, Auto-correlation, ARIMA, Cholesky, Copula, Normal Mixture distribution, Goodness-of-Fit, TNA Test |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10194785 |
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