LingFengGold/STReasoner

Official implementation of "STReasoner: Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning"

28
/ 100
Experimental

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.

time-series-analysis geospatial-analytics predictive-modeling urban-planning operations-intelligence
No Package No Dependents
Maintenance 10 / 25
Adoption 5 / 25
Maturity 13 / 25
Community 0 / 25

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Stars

14

Forks

Language

Python

License

MIT

Last pushed

Feb 20, 2026

Commits (30d)

0

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