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TimeMixer++

Model directory
thuml/timemixer-pp

~50M params | Upstream checkpoint

TimeMixer++ is THUML's multi-scale, multi-resolution time-series pattern machine and the successor to the ICLR 2024 TimeMixer architecture. Rather than specializing in a single objective, it is built as a general analytical model that supports 8 tasks including forecasting, classification, anomaly detection, and imputation.

Architecturally it is a multi-scale multi-resolution MLP-Mixer. It employs Multi-Resolution Time Imaging (MRTI) and Time Image Decomposition (TID) to extract patterns across both the time and frequency domains, then combines them through unified multi-scale mixing (MCM) and multi-resolution mixing (MRM) strategies that capture both fine-grained and macro-level structure. It is trained on multi-domain corpora spanning ETT, Weather, ECL, Traffic, Solar, PEMS, and the M4 competition datasets, totaling 10B+ observations.

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Model Classification

Family

TimeMixer

Type

time series foundation model

Pretrained time-series model for zero-shot or few-shot forecasting workloads.

Training Data

Multi-domain time-series corpora spanning ETT, Weather, ECL, Traffic, Solar, PEMS, and M4 competition datasets — 10B+ observations.

Recommended For

  • • Multi-task time-series analysis where you need forecasting, classification, and anomaly detection from one model
  • • Multi-resolution workloads spanning different temporal granularities

Strengths

  • • Broadest multi-task coverage among MLP-Mixer-based models in the catalog
  • • Multi-scale mixing captures both fine-grained and macro-level patterns

Limitations

  • • MLP-Mixer backbone may plateau on very long contexts compared to full-attention Transformers
  • • Less established in production deployments than the largest Transformer-based families

Capabilities

forecastingclassificationanomaly-detectionimputationmultivariatemulti-resolution

Tags

thumltimemixermulti-taskmulti-resolutionopen-source

Specifications

Parameters
~50M
Architecture
multi-scale multi-resolution MLP-Mixer (MCM + MRM)
Context length
n/a
Max context
n/a
Minimum history
n/a
Recommended history
n/a
Input step
n/a
Required target series
1
Temperature
Ignored
Top P
Ignored
Max output
n/a
License
n/a