Getting started with GluonTS

GluonTS is a flexible and extensible toolkit for probabilistic time series modeling using PyTorch. The toolkit provides state-of-the-art deep learning architectures specifically designed for time series tasks and an array of utilities for time series data processing, model evaluation, and experimentation.

The main objective of this section is to introduce the essential components of the gluonts library, emphasizing its core functionalities, adaptability, and user-friendliness.

Getting ready

To begin our journey, ensure that gluonts is installed as well as its backend dependency, pytorch:

pip install gluonts pytorch

With the installations complete, we can now dive into the capabilities of gluonts.

How to do it…

We start by accessing a sample dataset provided by the library:

from gluonts.dataset.repository.datasets import get_dataset
dataset = get_dataset("nn5_daily_without_missing", regenerate=False)

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