Data quality comes first at PredictHQ

Most raw event APIs are limited because they are secondary products for ticketing sites. But verified and intelligent data is PredictHQ's core business. Our team of data scientists has built more than 1,000 machine learning models to ensure the quality and accuracy of our data.
The quality of forecasts is directly related to high-quality data. If data quality is poor, forecasts will be off, which means schedules – fed by forecasts – will be sub-optimal, costing companies a lot of money.
Thomas Joseph
Head of Data Science
Legion
01

Aggregate

We aggregate events from hundreds of public and proprietary data sources. This depth and diversity is critical for verifying events and providing the most enriched and trustworthy event information.
02

Standardize

Global coverage of millions of events requires many sources, each with their own schema. PredictHQ does the hard work so every event has exactly the same format.
03

De-Duping

Duplicate events can seriously distort forecasting with false positives and break models. We've built a complex system to detect and remove them automatically.
04

Filter

Other event APIs contain 30% spam or add on events such as parking or VIP tickets. PredictHQ identifies and removes all spam events that have no impact on people movement and demand.

We’ve built the industry standard of event data quality

No one creates forecast grade data like PredictHQ. To use it in demand forecasting, you need a lot more than an event’s promotional copy. Without rigorous verification and enrichment, event data will ruin your models. That’s why we’ve created QSPD.
Most data isn’t in a standard format and is full of incorrect details with no validation creating major challenges to data science teams. We have more than a thousand machine learning models in our processing pipeline, plus a team dedicated to ensuring our data meets the highest quality standards. We use our knowledge graph and entity system to validate details and add intelligence layers to each event.

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