time seriesfamily
DLinear and NLinear
Simple linear forecasting baselines from the paper Are Transformers Effective for Time Series Forecasting? (Zeng et al., 2022), which matched or beat transformer models on long-term benchmarks.
Levelfamily
Classtime series
Parametersnot in the notes
Contextnot in the notes
Released26 May 2022
Opennessopen source
Runs onnot in the notes
Statusactive
Identity
- Developer
- The Chinese University of Hong Kong, IDEA Research (International Digital Economy Academy)
- Family
- not in the notes
- Released
- 26 May 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
- other One-layer linear models, with trend and seasonal decomposition (DLinear) or last-value normalisation (NLinear)
- 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 cure-lab/LTSF-Linear repository (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.
Links
DevelopersThe Chinese University of Hong Konguniversity, China
IDEA Research (International Digital Economy Academy)company, China
The Chinese University of Hong Konguniversity, ChinaIDEA Research (International Digital Economy Academy)company, ChinaModelDLinear and NLinearThe Chinese University of Hong Kong, time series
DLinear and NLinearThe Chinese University of Hong Kong, 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.
Organisations 2
Sources
- arxiv.org/abs/2205.13504 https://arxiv.org/abs/2205.13504
- github.com/cure-lab/LTSF-Linear https://github.com/cure-lab/LTSF-Linear
- nixtlaverse.nixtla.io/neuralforecast/docs/capabilities/overview.html https://nixtlaverse.nixtla.io/neuralforecast/docs/capabilities/overview.html
- arxiv.org/html/2205.13504 https://arxiv.org/html/2205.13504
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "DLinear and NLinear", https://aurion.semai.id/models/dlinear/, records of October 2026.