Wisdom of the Crowds in Forecasting: Forecast Summarization for Supporting Future Event Prediction
This work addresses the problem of unreliable traditional forecasting methods for complex events, which affects multiple domains and applications.
The authors tackled the problem of future event prediction by exploring the use of collective opinions, finding that cumulative perspectives can help estimate the likelihood of upcoming events, with no specific numbers reported. They organized existing research and frameworks on crowd wisdom for future event prediction.
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.