geo-infer-time

Analyze time-dependent patterns in geospatial data with ARIMA and exponential smoothing.

13|3|Updated May 13, 2025

Search Tags:#time series #forecasting #anomaly detection #geospatial #temporal analysis #spatiotemporal

Ready to run

npx skills add https://github.com/ActiveInferenceInstitute/GEO-INFER --skill geo-infer-time

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: geo-infer-time
Source: https://github.com/ActiveInferenceInstitute/GEO-INFER/tree/main/GEO-INFER-TIME
Command: npx skills add https://github.com/ActiveInferenceInstitute/GEO-INFER --skill geo-infer-time

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, statsmodels, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill tackles the challenge of analyzing and understanding time-dependent patterns within geospatial data, enabling accurate forecasting and anomaly detection.

Core Features & Use Cases

Time Series Analysis: Analyze trends, seasonality, and detect anomalies in temporal data.
Forecasting: Predict future values using methods like ARIMA and Exponential Smoothing.
Spatiotemporal Modeling: Integrate temporal analysis with geospatial context for richer insights.
Use Case: Predict future traffic congestion in a city by analyzing historical traffic data combined with weather patterns over time.

Quick Start

Use the geo-infer-time skill to analyze the time series data from the file 'sensor_readings.csv' to detect trends and seasonality.

Frequently Asked Questions about geo-infer-time

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I forecast future values from geospatial time series data?▼

You can forecast geospatial time series data using this Skill's built-in ARIMA and Exponential Smoothing models. It analyzes historical spatiotemporal patterns to predict future values, such as estimating traffic congestion based on past sensor readings and weather trends.

What is spatiotemporal analysis and how does it handle time-dependent geospatial data?▼

Spatiotemporal analysis integrates temporal time series analysis with geospatial context to uncover time-dependent patterns. This Skill uses statistical and machine learning techniques to decompose temporal trends, detect seasonality, and identify anomalies within spatial data.

Can I use pandas and numpy for time series anomaly detection in CSV files?▼

Yes, you can load CSV files using pandas and process them with numpy to perform time series anomaly detection. This Skill leverages those dependencies alongside scipy and statsmodels to identify irregular patterns and outliers in your temporal data.

Does statsmodels support time series decomposition for sensor readings?▼

Statsmodels supports time series decomposition for sensor readings by separating data into trend, seasonal, and residual components. This Skill utilizes those capabilities to analyze and model temporal patterns from your raw geospatial data files.

What's the best way to analyze historical traffic data combined with weather patterns?▼

The best way to analyze historical traffic data with weather patterns is through spatiotemporal modeling. This Skill integrates temporal analysis with geospatial context, allowing you to forecast future traffic congestion by correlating multiple time-dependent variables.