time seriesfamily
MSTL
Seasonal-trend decomposition algorithm for series with several seasonal patterns, used as a forecasting model in statsforecast. Paper by Bandara et al. (2021).
Levelfamily
Classtime series
Parametersnot in the notes
Contextnot in the notes
Released28 Jul 2021
Opennessopen source
Runs onnot in the notes
Statusactive
R&D Topics
Identity
- Developer
- University of Melbourne
- Family
- not in the notes
- Released
- 28 Jul 2021 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 Seasonal-trend decomposition for multiple seasonal periods
- 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 Nixtla statsforecast implementation (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
ModelMSTLUniversity of Melbourne, time series
MSTLUniversity of Melbourne, time seriesR&D TopicsDeep Learning Architectures for Time-Series Forecasting and ClassificationTime-Series Foundation Models
Use CasesIndustrial Forecasting (Load, Throughput, Demand)IndustrialDemand ForecastingRetail and LogisticsAnomaly Detection on Sensor, SCADA and Historian DataIndustrialEnergy OptimisationIndustrial+1 moreUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026
PT Pertamina Hulu Rokan (PHR)In production, 17 Oct 2025MIND IDResearch, 14 Oct 2025
Shenzhen Energy GroupIn production, 23 Jun 2025+4 moreDeployments of these use cases; none names this model.
PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026PT Pertamina Hulu Rokan (PHR)In production, 17 Oct 2025MIND IDResearch, 14 Oct 2025Shenzhen Energy GroupIn production, 23 Jun 2025+4 moreDeployments of these use cases; none names this model.Organisations 1
Sources
- arxiv.org/abs/2107.13462 https://arxiv.org/abs/2107.13462
- github.com/Nixtla/statsforecast https://github.com/Nixtla/statsforecast
- nixtlaverse.nixtla.io/statsforecast/index.html https://nixtlaverse.nixtla.io/statsforecast/index.html
- arxiv.org/html/2107.13462 https://arxiv.org/html/2107.13462
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "MSTL", https://aurion.semai.id/models/mstl/, records of October 2026.