time seriesfamily
PatchTST
Transformer for long-term forecasting that splits each series into patches treated as tokens, from the paper A Time Series is Worth 64 Words (Nie et al., 2022).
Levelfamily
Classtime series
Parametersnot in the notes
Contextnot in the notes
Released27 Nov 2022
Opennessopen source
Runs onnot in the notes
Statusactive
Identity
- Developer
- Princeton University, IBM
- Family
- not in the notes
- Released
- 27 Nov 2022 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
- dense transformer Channel-independent transformer on patches of the 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 official yuqinie98/PatchTST repository (Apache-2.0). Not the same as IBM PatchTST-FM, which AURION holds separately.
- 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.
Links
ModelPatchTSTPrinceton University, time series
PatchTSTPrinceton University, time seriesUse CasesIndustrial Forecasting (Load, Throughput, Demand)IndustrialDemand ForecastingRetail and LogisticsUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs Itnone recordedDeployments of these use cases; none names this model.
Sources
- arxiv.org/abs/2211.14730 https://arxiv.org/abs/2211.14730
- github.com/yuqinie98/PatchTST https://github.com/yuqinie98/PatchTST
- nixtlaverse.nixtla.io/neuralforecast/docs/capabilities/overview.html https://nixtlaverse.nixtla.io/neuralforecast/docs/capabilities/overview.html
- arxiv.org/html/2211.14730 https://arxiv.org/html/2211.14730
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "PatchTST", https://aurion.semai.id/models/patchtst/, records of October 2026.