| name | geo-infer-time |
| description | Time series analysis and temporal modeling for geospatial data. Use when analyzing temporal patterns, forecasting spatial time series, detecting change points, or working with spatio-temporal datasets. |
| prerequisites | {"required":["geo-infer-math"],"recommended":["geo-infer-data"]} |
| difficulty | intermediate |
| estimated_time | 45min |
| examples_dir | ../GEO-INFER-EXAMPLES/examples/ |
GEO-INFER-TIME
Instructions
Core Capabilities
- Time series analysis: Decomposition, trend detection, seasonality
- Forecasting: ARIMA, exponential smoothing, temporal GP
- Change detection: CUSUM, Bayesian change points, structural breaks
- Temporal indexing: Time-aware spatial queries, temporal resolution management
- Spatio-temporal: Joint analysis of spatial and temporal dimensions
Key Imports
from geo_infer_time.core.time_series import TimeSeriesAnalyzer
from geo_infer_time.core.forecasting import Forecaster
from geo_infer_time.core.change_detection import ChangePointDetector
Examples
from geo_infer_time.core.time_series import TimeSeriesAnalyzer
analyzer = TimeSeriesAnalyzer(frequency="daily")
decomposition = analyzer.decompose(series, method="stl")
trend = decomposition.trend
seasonal = decomposition.seasonal
Guidelines
Integrations
- Integrates with SPACE for spatio-temporal analysis
- ISO 8601 for all datetime handling
- Test:
uv run python -m pytest GEO-INFER-TIME/tests/ -v
