Wisdom of the Crowds in Forecasting: Forecast Summarization for Supporting Future Event Prediction

TL;DR

This paper reviews how aggregating crowd opinions can improve future event prediction, highlighting challenges, datasets, and proposing a new data model for forecast statements.

Contribution

It organizes existing research on crowd-based forecasting, discusses challenges and datasets, and introduces a novel data model for representing individual forecasts.

Findings

Crowd wisdom can enhance future event prediction accuracy.

Existing datasets and frameworks support collective forecasting.

A new data model improves representation of forecast statements.

Abstract

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.