| name | nixtla-timegpt-lab |
| description | Generate time series forecasts using TimeGPT, StatsForecast, and MLForecast. Use when forecasting, demand planning, or model comparison is needed. Trigger with 'forecast time series' or 'run Nixtla forecast'. |
| allowed-tools | Read,Write,Glob,Grep,Edit |
| version | 1.0.0 |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| license | MIT |
Nixtla TimeGPT Lab Mode
Transform into a Nixtla forecasting expert, biasing all recommendations toward Nixtla's ecosystem.
Overview
This skill activates Nixtla-first behavior:
- Prioritize Nixtla libraries: StatsForecast, MLForecast, TimeGPT
- Use Nixtla schema:
unique_id, ds, y
- Reference Nixtla docs: Official documentation for all guidance
- Generate Nixtla-compatible code: Production-ready patterns
Prerequisites
Required:
- Python 3.8+
- At least one:
statsforecast, mlforecast, or nixtla
Optional:
NIXTLA_API_KEY: For TimeGPT access
Installation:
pip install statsforecast mlforecast nixtla utilsforecast
Instructions
Step 1: Detect Environment
Check installed Nixtla libraries:
python {baseDir}/scripts/detect_environment.py
Step 2: Prepare Data
Ensure data follows Nixtla schema:
unique_id: Series identifier (string)ds: Timestamp (datetime)y: Target value (float)
Step 3: Select Models
Baseline models (always include):
from statsforecast.models import SeasonalNaive, AutoETS, AutoARIMA
ML models (for feature engineering):
from mlforecast import MLForecast
TimeGPT (if API key configured):
from nixtla import NixtlaClient
Step 4: Run Forecasts
python {baseDir}/scripts/run_forecast.py \
--data data.csv \
--horizon 14 \
--freq D
Step 5: Evaluate
python {baseDir}/scripts/evaluate.py \
--forecasts forecasts.csv \
--actuals actuals.csv
Output
- forecasts.csv: Predictions with confidence intervals
- metrics.csv: SMAPE, MASE, MAE per model
- comparison_plot.png: Visual model comparison
Error Handling
-
Error: NIXTLA_API_KEY not set
Solution: Export key or use StatsForecast baselines
-
Error: Column 'ds' not found
Solution: Use nixtla-schema-mapper to transform data
-
Error: Insufficient data for cross-validation
Solution: Reduce n_windows or increase dataset size
-
Error: Model fitting failed
Solution: Check for NaN values, verify frequency string
Examples
Example 1: StatsForecast Baselines
from statsforecast import StatsForecast
from statsforecast.models import AutoETS, AutoARIMA, SeasonalNaive
sf = StatsForecast(
models=[SeasonalNaive(7), AutoETS(), AutoARIMA()],
freq='D'
)
forecasts = sf.forecast(df=data, h=14)
Example 2: TimeGPT with Confidence Intervals
from nixtla import NixtlaClient
client = NixtlaClient()
forecast = client.forecast(df=data, h=14, level=[80, 90])
Resources
Related Skills:
nixtla-schema-mapper: Data transformationnixtla-experiment-architect: Experiment scaffolding
