# Darts — User-Friendly Time Series Forecasting and Anomaly Detection

> A Python library for easy manipulation and forecasting of time series, supporting both classical statistical models and modern deep learning architectures with a unified API.

## Install

Save as a script file and run:

# Darts — User-Friendly Time Series Forecasting and Anomaly Detection

## Quick Use
```bash
pip install darts
```
```python
from darts.datasets import AirPassengersDataset
from darts.models import ExponentialSmoothing
series = AirPassengersDataset().load()
model = ExponentialSmoothing()
model.fit(series)
prediction = model.predict(12)
```

## Introduction
Darts is an open-source Python library developed by Unit8 that makes time series forecasting accessible to both beginners and experts. It wraps a wide range of models — from ARIMA and exponential smoothing to N-BEATS and Temporal Fusion Transformer — behind a consistent, easy-to-use API centered around its own TimeSeries data structure.

## What Darts Does
- Provides a unified `fit()`/`predict()` interface across 30+ forecasting models
- Supports probabilistic forecasting with confidence intervals and quantile predictions
- Handles multivariate, multi-step, and hierarchical time series natively
- Includes built-in anomaly detection and scoring modules
- Offers data preprocessing utilities for scaling, missing value handling, and feature engineering

## Architecture Overview
Darts is built around the `TimeSeries` class, an immutable wrapper over xarray that stores time-indexed data with optional static covariates. Models inherit from base classes (`LocalForecastingModel`, `GlobalForecastingModel`) that define the training interface. Deep learning models use PyTorch Lightning under the hood, enabling GPU training, early stopping, and learning rate scheduling out of the box.

## Self-Hosting & Configuration
- Install via `pip install darts` or `pip install "u8darts[all]"` for all optional dependencies
- Use `darts[torch]` to include PyTorch-based models only
- Configure deep learning models with `pl_trainer_kwargs` for PyTorch Lightning options
- Set `num_loader_workers` for parallel data loading during training
- Export trained models with `model.save()` and reload with `Model.load()`

## Key Features
- Consistent API across statistical, machine learning, and deep learning models
- First-class support for covariates (past and future external variables)
- Backtesting and evaluation with `historical_forecasts()` and multiple metrics
- Ensemble models and pipeline composition for stacking approaches
- Rich plotting utilities built on matplotlib

## Comparison with Similar Tools
- **sktime** — Broader ML task coverage; Darts focuses on making forecasting simple
- **statsmodels** — Lower-level statistical API; Darts wraps statsmodels with a friendlier interface
- **GluonTS** — AWS probabilistic forecasting; Darts is framework-agnostic and easier to start
- **Prophet** — Single model optimized for business series; Darts offers 30+ model choices
- **NeuralForecast** — Pure neural focus; Darts mixes classical and deep models

## FAQ
**Q: Can Darts handle multiple time series at once?**
A: Yes. Global models in Darts can be trained on multiple series simultaneously and transfer learned patterns across them.

**Q: Does Darts support GPU training?**
A: Yes. All deep learning models leverage PyTorch Lightning and can be trained on GPUs by passing appropriate trainer kwargs.

**Q: What data formats does Darts accept?**
A: Darts accepts pandas DataFrames, NumPy arrays, or its native TimeSeries objects.

**Q: Is Darts suitable for production use?**
A: Yes. Models can be serialized, and the library supports reproducible pipelines suitable for deployment.

## Sources
- https://github.com/unit8co/darts
- https://unit8co.github.io/darts/

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Source: https://tokrepo.com/en/workflows/asset-9fc23920
Author: Script Depot