Project AURIONAI Use-Cases Research & Innovation
time seriesfamily

DeepAR

Probabilistic forecasting method that trains an autoregressive recurrent network across many related time series. Paper by Salinas et al. (2017).

AmazonAmazonUSA
Amazon
Levelfamily
Classtime series
Parametersnot in the notes
Contextnot in the notes
Released13 Apr 2017
Opennessopen source
Runs onnot in the notes
Statusactive

R&D Topics

Deep learning architectures for time-series forecasting and classificationproduced by

Identity

Developer
Amazon
Family
not in the notes
Released
13 Apr 2017 sourcesannouncement page
Weights published
–
Record checked
12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.

Architecture and Size

Architecture
autoregressive Autoregressive recurrent network trained on many related series
Total parameters
– sources
Active parameters
–
Checkpoint parameters
–
Context length
not in the notes
Input
time series
Output
time series
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).

Openness and Access

Openness
open source
Licence
Apache License 2.0 sources
Access routes
not in the notes
Where this comes from
The licence file governs over registry tags and aggregator labels.

Deployment

Runs on
not in the notes
Footprint
Licence is that of the GluonTS and neuralforecast implementations (Apache-2.0).
Runtimes
not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.

Indonesia

Indonesian support
not stated
Regional languages
not in the notes
Jakarta hosting
unknown
Languages
not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.

Benchmark Results

The notes record no benchmark result for this model.

Lineage

The notes record no lineage for this model.

Industry Use Cases

No industry use case links this model.

Sources

  1. arxiv.org/abs/1704.04110 https://arxiv.org/abs/1704.04110
  2. github.com/awslabs/gluonts https://github.com/awslabs/gluonts
  3. github.com/Nixtla/neuralforecast https://github.com/Nixtla/neuralforecast
  4. nixtlaverse.nixtla.io/neuralforecast/docs/capabilities/overview.html https://nixtlaverse.nixtla.io/neuralforecast/docs/capabilities/overview.html
  5. arxiv.org/html/1704.04110 https://arxiv.org/html/1704.04110

To cite this page: Project AURION, AI Use-Cases Research & Innovation, "DeepAR", https://aurion.semai.id/models/deepar/, records of October 2026.