LingFengGold/STReasoner
Official implementation of "STReasoner: Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning"
STReasoner helps operations engineers, city planners, and environmental scientists analyze complex sensor data or activity logs to understand how events unfold across time and space. It takes in structured spatio-temporal time series data, like traffic patterns or weather readings, and outputs insights into future trends, relationships between different locations, or the root causes of incidents. This tool is for professionals who need to make data-driven decisions based on dynamic, interconnected spatial and temporal information.
Use this if you need to build or fine-tune an advanced AI model to perform complex reasoning tasks on time series data that varies both geographically and over time.
Not ideal if you're looking for a simple, off-the-shelf application to visualize or do basic forecasting on standard time series without the need for deep causal or relational reasoning.
Stars
14
Forks
—
Language
Python
License
MIT
Category
Last pushed
Feb 20, 2026
Commits (30d)
0
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