Forecast Mining
Prediction markets generate large amounts of data through prices, trades, and probability updates. Forecast mining analyzes this data to understand which signals tend to be informative and which ones add noise.
The goal is not just to read current probabilities, but to study how forecasts evolve. This includes tracking accuracy, timing of belief shifts, and how markets react to new information. Forecast mining often examines differences between early and late forecasts. It can reveal whether markets converge toward correct outcomes or overshoot due to hype or uncertainty.
Analysts also use forecast mining to compare similar markets across events or time periods. This helps identify structural patterns, recurring biases, and conditions where prediction markets perform best. Over time, these insights are used to improve calibration, weighting strategies, and confidence assessment. This makes prediction markets data more useful for long-term analysis and decision support.
Why It Matters
Forecast mining helps turn prediction markets from point-in-time signals into long-term learning tools. It allows users to improve interpretation, modeling, and trust in market-based forecasts.
What is forecast mining in prediction markets?
In prediction markets, forecast mining means analyzing historical forecasts to understand their behavior and accuracy. It looks at how probabilities change, when they improve, and when they fail. This helps identify which market signals are most useful. Over time, it supports better forecasting practices.
How is forecast mining done using prediction markets data?
Forecast mining uses prediction markets data such as price histories, volume trends, and resolution outcomes. Analysts compare forecasts against actual results to measure performance. Patterns like early accuracy or late overreaction can be identified. These insights help refine models and expectations.
Why is forecast mining useful when working with prediction markets APIs?
Prediction markets APIs make forecast mining scalable by providing consistent historical and real-time data. Analysts can automate data collection and run large-scale evaluations across markets. This supports research, backtesting, and model improvement. APIs enable continuous learning from market behavior.
Real-World Example
On Polymarket, analysts may study how election market probabilities evolved over months. Forecast mining can reveal when markets became accurate and which events caused meaningful belief updates.
Forecast Mining and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides structured prediction markets data suitable for forecast mining. Analysts can access historical probabilities, trading activity, and resolution data. This supports backtesting, performance analysis, and forecasting research. The API enables systematic extraction of insights across prediction markets.