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How to use Darts: what you can do with it

What is Darts? Darts is a Python library for forecasting and anomaly detection on time series. Every forecasting model, from ARIMA to N-BEATS, uses the same fit and predict interface, like scikit-learn. It supports univariate and multivariate series, training one model on many series, probabilistic forecasts, past and future covariates, backtesting, and a darts.ad module of anomaly scorers and detectors including a PyODScorer.

How do I use it? Install it with pip install darts; then create the series with TimeSeries.from_dataframe, naming the time column and the value column; Split it, for example with train, val = series[:-36], series[-36:], then fit ExponentialSmoothing and call model.predict(len(val), num_samples=1000). Full steps below.

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What do you want to do?

3 use cases

Quick start with Darts

  1. Check you have what it needs: 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.
  2. Open unit8co/darts on GitHub and read the README and license.
  3. Install it with pip install darts
  4. Create the series with TimeSeries.from_dataframe, naming the time column and the value column
  5. Split it, for example with train, val = series[:-36], series[-36:], then fit ExponentialSmoothing and call model.predict(len(val), num_samples=1000)
  6. Plot with series.plot() and prediction.plot(label="forecast", low_quantile=0.05, high_quantile=0.95)

These steps come from our top use case, Get a probabilistic forecast in a few lines, which also has a copy-paste agent prompt.

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About this repo

  • Who it is forData scientists who want to compare many forecasting models without rewriting their code each time.
  • Why it is hot9.6k stars, picked up by one AI source we track
  • LanguagePython
  • Sourcegithub.com/unit8co/darts ยท Back to Hot repos

Requirements

  • 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

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Questions about Darts

What is Darts?

Darts is a Python library for forecasting and anomaly detection on time series. Every forecasting model, from ARIMA to N-BEATS, uses the same fit and predict interface, like scikit-learn. It supports univariate and multivariate series, training one model on many series, probabilistic forecasts, past and future covariates, backtesting, and a darts.ad module of anomaly scorers and detectors including a PyODScorer. It is an open-source project on GitHub: https://github.com/unit8co/darts.

How do I use Darts?

Install it with pip install darts; then create the series with TimeSeries.from_dataframe, naming the time column and the value column; Split it, for example with train, val = series[:-36], series[-36:], then fit ExponentialSmoothing and call model.predict(len(val), num_samples=1000). The use cases on this page give the full steps and a copy-paste agent prompt for each job.

Who is Darts for?

Data scientists who want to compare many forecasting models without rewriting their code each time.

What can I do with Darts?

3 use cases so far, for example: Get a probabilistic forecast in a few lines; Flag anomalies in a multivariate series; Compare many models on the same series fairly. Each has steps and a copy-paste agent prompt.

What do I need to run Darts?

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.

What are alternatives to Darts?

Other hot AI repos in Data and analytics: Chronos Forecasting, Crawlee Python, DLT, Timesfm, Ultralytics.

Is Darts safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.