Event Timeline Dataset
In prediction markets, probabilities move in response to information over time. An event timeline dataset organizes these information points into a clear sequence, such as announcements, data releases, or milestones.
The dataset links external events to market behavior. It shows when information became available and how markets reacted before and after each moment. Event timelines help distinguish cause from coincidence. Analysts can see whether probability shifts align with real developments or occur without clear triggers.
They are especially useful for complex or long-running events. When many updates happen over weeks or months, a structured timeline prevents misinterpretation.
For analysts, event timeline datasets add essential context to prediction markets data. They make it easier to explain belief shifts, volatility spikes, and periods of inactivity. Over time, timelines support deeper analysis. They help evaluate reaction speed, attention cycles, and forecasting discipline across markets.
Why It Matters
Without timing context, probability changes can be misleading. Event timeline datasets help users interpret prediction markets data accurately and understand why markets move.
What is an event timeline dataset in prediction markets?
In prediction markets, an event timeline dataset is a chronological list of relevant updates tied to an event. It includes moments that could influence beliefs and trading behavior. This helps connect external reality with market reactions. It is a core tool for contextual analysis.
How does an event timeline dataset improve prediction markets data analysis?
Event timeline datasets allow analysts to align probability changes with real-world developments. This makes it easier to identify genuine belief updates versus noise. Analysts can measure reaction delays and overreaction patterns. The result is clearer and more reliable analysis.
Why is an event timeline dataset important for prediction markets APIs?
Prediction markets APIs often deliver probabilities without external context. Event timeline datasets complement API data by adding structured time markers. Analysts can merge timelines with market data to improve modeling and interpretation. APIs make it possible to synchronize timelines across many markets.
Real-World Example
On Kalshi, an economic indicator market may include a timeline of scheduled releases, revisions, and confirmations. Analysts use this timeline to explain probability changes around each update.
Event Timeline Dataset and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides time-stamped prediction markets data that pairs naturally with event timeline datasets. Analysts can align probability movements with external event timestamps. This supports reaction analysis, forecasting evaluation, and behavioral studies. The API enables consistent integration of timelines with prediction markets data.