Chrono - Linguistic Pattern Forecasting
Hackathon project forecasting linguistic patterns to predict how language evolves over time.
Chrono is a forecasting system designed to predict linguistic patterns into the future, built during QHacks 2025. The project explores how language evolves over time using machine learning techniques.
Project Architecture
The system is organized into five main components:
- Polly: Handles text-to-speech and voice synthesis
- Data Preprocessing: Manages data preparation workflows
- Data Pipeline: Orchestrates data flow through the system
- Forecast: Contains prediction and forecasting models
- Server: Backend infrastructure for deployment
Technical Implementation
Machine Learning Pipeline
Built an end-to-end ML pipeline that processes linguistic data, trains forecasting models, and generates predictions about language evolution patterns.
Voice Synthesis Integration
Integrated AWS Polly for voice synthesis capabilities, allowing the system to vocalize predicted linguistic patterns.
Team & Development
- Led a team of 4 contributors
- 52 commits over a 36-hour hackathon
- Primary language: Python (89.9% of codebase)
- Web interface built with HTML/CSS
Hackathon Context
Built at QHacks 2025, Queen's University's annual hackathon, demonstrating rapid prototyping and team collaboration skills under time pressure.