nixtla-experiment-architect

name	nixtla-experiment-architect
description	Scaffolds production-ready forecasting experiments with Nixtla libraries. Creates configuration files, experiment harnesses, multi-model comparisons, and cross-validation workflows for StatsForecast, MLForecast, and TimeGPT. Activates when user needs experiment setup, forecasting pipeline creation, model benchmarking, or multi-model comparison framework.
allowed-tools	Read,Write,Glob,Grep,Edit
version	1.0.0
license	MIT

Nixtla Experiment Architect

Design and scaffold complete forecasting experiments using Nixtla's libraries.

Overview

This skill creates production-ready experiment harnesses:

• Configuration management: YAML-based experiment config
• Multi-model comparison: StatsForecast + MLForecast + TimeGPT
• Cross-validation: Rolling-origin or expanding-window
• Metrics evaluation: SMAPE, MASE, MAE, RMSE

Prerequisites

Required:

• Python 3.8+
• statsforecast, utilsforecast

Optional:

• mlforecast: For ML models
• nixtla: For TimeGPT
• NIXTLA_API_KEY: TimeGPT access

Installation:

pip install statsforecast mlforecast nixtla utilsforecast pyyaml

Instructions

Step 1: Gather Requirements

Collect experiment parameters:

• Data source path
• Target column name
• Forecast horizon (e.g., 14 days)
• Frequency (D, H, W, M)
• Unique ID column (optional)

Step 2: Generate Configuration

python {baseDir}/scripts/generate_config.py \
    --data data/sales.csv \
    --target sales \
    --horizon 14 \
    --freq D \
    --output forecasting/config.yml

Step 3: Scaffold Experiment

python {baseDir}/scripts/scaffold_experiment.py \
    --config forecasting/config.yml \
    --output forecasting/experiments.py

Step 4: Run Experiment

python forecasting/experiments.py

Step 5: Review Results

cat forecasting/results/metrics_summary.csv

Output

• forecasting/config.yml: Experiment configuration
• forecasting/experiments.py: Runnable experiment harness
• forecasting/results/: Metrics and forecasts (after running)

Error Handling

1. Error: Data file not found Solution: Verify data source path in config

2. Error: Column not found Solution: Check column names match your data

3. Error: Missing required package Solution: Install missing dependencies with pip

4. Error: Cross-validation failed Solution: Ensure enough data for n_windows

Examples

Example 1: Daily Sales Forecast

python {baseDir}/scripts/generate_config.py \
    --data data/sales.csv \
    --target revenue \
    --horizon 30 \
    --freq D \
    --id_col store_id

Output config.yml:

data:
  source: data/sales.csv
  target: revenue
  unique_id: store_id
forecasting:
  horizon: 30
  freq: D
models:
  - SeasonalNaive
  - AutoETS
  - AutoARIMA

Example 2: Hourly Energy Forecast

python {baseDir}/scripts/generate_config.py \
    --data data/energy.csv \
    --target consumption \
    --horizon 24 \
    --freq H

Resources

• Scripts: {baseDir}/scripts/
• Templates: {baseDir}/assets/templates/
• Nixtla Docs: https://nixtla.github.io/

Related Skills:

• nixtla-timegpt-lab: Core forecasting guidance
• nixtla-schema-mapper: Data transformation
• nixtla-prod-pipeline-generator: Production deployment