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TTM-R3

online
ibm-research/ttm-r3

~1.4M (Lite) to ~35M params | 512 context | $0.00025 per forecast | CC-BY-NC-SA-4.0

TTM-R3 is IBM Research's March 31, 2026 refresh of the TinyTimeMixer family. While still classified as `tinytimemixer` under the hood, R3 adds trend-residual decomposition, a multi-quantile probabilistic forecasting head, gated attention, FFT-based frequency embeddings, and learnable sequence-level register tokens, while preserving the compact ~1.4M–35M parameter footprint that makes TTM practical on CPUs and lightweight hosted inference. IBM reports a 15–50x inference speedup vs state-of-the-art forecasters and a meaningful accuracy improvement over TTM-R2, but the current upstream checkpoint and toolkit path still emit uninitialized-weight warnings during direct load, so short deterministic continuation quality should be validated on your own holdout before production reliance. Hosted on TSFM.ai under a pass-through compute posture: TTM-R3 ships under the CC-BY-NC-SA-4.0 license (research / non-commercial use only), so you are responsible for ensuring your intended use falls within the upstream license — see section 7 of our Terms of Service.

Model Classification

Family

TinyTimeMixer

Type

time series foundation model

Pretrained time-series model exposed on TSFM.ai for zero-shot or few-shot forecasting workloads.

Training Data

IBM's GiftEvalPretrain subset plus KernelSynth-style synthetic augmentation; corpus aligned with the TTM family rather than narrowed to a single benchmark.

Recommended For

  • CPU-friendly or latency-sensitive forecasting baselines
  • Fast zero-shot checks before escalating to larger TSFMs

Strengths

  • Very small checkpoints with efficient deployment characteristics
  • Useful lightweight baseline for standard public forecasting workloads

Limitations

  • Lower ceiling than larger modern TSFM families on broad zero-shot leaderboards
  • Checkpoint families are tuned around specific context and prediction settings
  • The hosted TTM-R3 path still needs careful validation on short deterministic trend extrapolation before using it as a default production forecaster

Not Ideal For

  • Default routing for short smooth trend-continuation workloads without a holdout check
  • Commercial production use unless the upstream CC-BY-NC-SA-4.0 license fits your use case

Capabilities

forecastingquantile-forecastingprobabilistic-forecastingmultivariatezero-shothigh-throughput

Tags

ibmibm-researchttmtinyresearch-licensenon-commercialprobabilistic

Specifications

Parameters
~1.4M (Lite) to ~35M
Architecture
TinyTimeMixer with trend-residual decomposition, gated attention, multi-quantile head, and FFT embeddings
Context length
512
Max context
512
Minimum history
n/a
Recommended history
512
Input step
n/a
Required target series
1
Temperature
Ignored
Top P
Ignored
Max output
1,024
Avg latency
n/a
Uptime
n/a
Plan limits
1,000 rpm free · 1,000,000 rpm with billing
Accelerator
T4
Regions
Virginia, US
License
CC-BY-NC-SA-4.0

Pricing

Per forecast
$0.00025

Performance

Average latency
n/a
Availability
n/a
Plan limits
1,000 rpm free · 1,000,000 rpm with billing