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How to compare many models on the same series fairly with Darts

Use the shared interface, metrics and backtesting utilities to rank models on historical forecasts.

Short answer: Install it with pip install darts; then build your TimeSeries and pick candidates from the forecasting models listed in the README, from ARIMA to N-BEATS; use the backtesting utilities, which the README describes as simulating historical forecasts using moving time windows. All steps.

  • Data and analytics
  • Intermediate
  • 2 hours
  • From the README
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Who it is forA team that has to defend which model goes into production.

The problemComparing libraries one by one makes results hard to trust.

The outcomeA ranked comparison of models on your own data, using moving time windows.

What you need first

  • Python 3.11 or newer, ideally in a clean environment
  • `pip install darts`
  • Your series as a pandas DataFrame or one of the supported backends

Steps

  1. Install it with pip install darts
  2. Build your TimeSeries and pick candidates from the forecasting models listed in the README, from ARIMA to N-BEATS
  3. Use the backtesting utilities, which the README describes as simulating historical forecasts using moving time windows
  4. Score the runs with the metrics module, from R2-scores to Mean Absolute Scaled Error, and explain the winner with SHAP values where supported

Prompt for your agent

Using unit8co/darts, backtest ARIMA and a deep model on <my series> with moving windows, report the metrics side by side and say which one I should ship.

Caveat. Deep learning models in Darts run on PyTorch Lightning, so training time depends on your hardware.

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Using the Deduped MCP, get the setup steps for unit8co/darts and "Compare many models on the same series fairly"

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