Welcome to the Luna Modelling API
The Luna Modelling API provides advanced statistical modeling and filtering services designed for educational research and student performance analysis. Built with robust quota management and persistent data storage, our API enables researchers and institutions to process time series data efficiently.Explore the endpoints below using the live OpenAPI-powered playground.
Switch between request examples to understand stateless and stateful Kalman
filter runs.
Getting Started
To start using the Luna Modelling API:- Contact us to obtain your API key and account ID
- Include your API key in the
X-API-Keyheader with every request - Monitor your quota using the
/account/quotaendpoint - Process your data with our Kalman filtering service
Base URL
Specification
Luna Modelling API
Download the OpenAPI specification that powers this reference.
Authentication
All endpoints require an API key via theX-API-Key header. The server uses the
key to validate access, enforce quotas, and bind the request to the supplied
account_id path parameter when relevant.
Available Endpoints
Account Management
GET /account/quota- Check your remaining quota before issuing modelling requests. Use this to monitor usage and plan your API calls accordingly.
Data Processing
POST /{account_id}/kalman- Run Kalman filtering on one or more time series, with optional persistence. The Rauch-Tung-Striebel smoother reduces noise and provides smoothed predictions for student performance data.
Use Cases
The Luna Modelling API is designed for:- Student Performance Prediction - Filter noisy grade data to identify true performance trends
- Early Warning Systems - Detect students at risk of dropout through statistical analysis
- Longitudinal Studies - Accumulate and analyze student data over multiple semesters
- Research Projects - Process educational data with state-of-the-art filtering techniques