Time series forecasting has a wide range of applications: finance, retail, healthcare, IoT, etc. Recently deep learning models such as ESRNN or N-BEATS have proven to have state-of-the-art performance in these tasks. Nixtlats is a python library that we have developed to facilitate the use of these state-of-the-art models to data scientists and developers, so that they can use them in productive environments. Written in pytorch, its design is focused on usability and reproducibility of experiments. For this purpose, nixtlats has several modules:
Data: contains datasets of various time series competencies.
Models: includes state-of-the-art models.
Evaluation: has various loss functions and evaluation metrics.
Objective:
- To introduce attendees to the challenges of time series forecasting with deep learning.
- Commercial applications of time series forecasting.
- Describe nixtlats, their components and best practices for training and deploying state-of-the-art models in production.
- Reproduction of state-of-the-art results using nixtlats from the winning model of the M4 time series competition (ESRNN).
Project repository: github.com/Nixtla/nixtlats.
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