Wisdom of the Crowds in Forecasting: Forecast Summarization for Supporting Future Event Prediction
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910822799769600 |
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| author | Saha, Anisha Jatowt, Adam |
| author_facet | Saha, Anisha Jatowt, Adam |
| contents | Future Event Prediction (FEP) is an essential activity whose demand and application range across multiple domains. While traditional methods like simulations, predictive and time-series forecasting have demonstrated promising outcomes, their application in forecasting complex events is not entirely reliable due to the inability of numerical data to accurately capture the semantic information related to events. One forecasting way is to gather and aggregate collective opinions on the future to make predictions as cumulative perspectives carry the potential to help estimating the likelihood of upcoming events. In this work, we organize the existing research and frameworks that aim to support future event prediction based on crowd wisdom through aggregating individual forecasts. We discuss the challenges involved, available datasets, as well as the scope of improvement and future research directions for this task. We also introduce a novel data model to represent individual forecast statements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08205 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Wisdom of the Crowds in Forecasting: Forecast Summarization for Supporting Future Event Prediction Saha, Anisha Jatowt, Adam Machine Learning Computation and Language Information Retrieval Future Event Prediction (FEP) is an essential activity whose demand and application range across multiple domains. While traditional methods like simulations, predictive and time-series forecasting have demonstrated promising outcomes, their application in forecasting complex events is not entirely reliable due to the inability of numerical data to accurately capture the semantic information related to events. One forecasting way is to gather and aggregate collective opinions on the future to make predictions as cumulative perspectives carry the potential to help estimating the likelihood of upcoming events. In this work, we organize the existing research and frameworks that aim to support future event prediction based on crowd wisdom through aggregating individual forecasts. We discuss the challenges involved, available datasets, as well as the scope of improvement and future research directions for this task. We also introduce a novel data model to represent individual forecast statements. |
| title | Wisdom of the Crowds in Forecasting: Forecast Summarization for Supporting Future Event Prediction |
| topic | Machine Learning Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2502.08205 |