  Large pre-trained models excel in zero/few-shot learning for language and
vision tasks but face challenges in multivariate time series (TS) forecasting
due to diverse data characteristics. Consequently, recent research efforts have
focused on developing pre-trained TS forecasting models. These models, whether
built from scratch or adapted from large language models (LLMs), excel in
zero/few-shot forecasting tasks. However, they are limited by slow performance,
high computational demands, and neglect of cross-channel and exogenous
correlations. To address this, we introduce Tiny Time Mixers (TTM), a compact
model (starting from 1M parameters) with effective transfer learning
capabilities, trained exclusively on public TS datasets. TTM, based on the
light-weight TSMixer architecture, incorporates innovations like adaptive
patching, diverse resolution sampling, and resolution prefix tuning to handle
pre-training on varied dataset resolutions with minimal model capacity.
Additionally, it employs multi-level modeling to capture channel correlations
and infuse exogenous signals during fine-tuning. TTM outperforms existing
popular benchmarks in zero/few-shot forecasting by (4-40\%), while reducing
computational requirements significantly. Moreover, TTMs are lightweight and
can be executed even on CPU-only machines, enhancing usability and fostering
wider adoption in resource-constrained environments. Model weights for our
initial variant (TTM-Q) are available at
https://huggingface.co/ibm-granite/granite-timeseries-ttm-v1. Model weights for
more sophisticated variants (TTM-B, TTM-E, and TTM-A) will be shared soon. The
source code for TTM can be accessed at
https://github.com/ibm-granite/granite-tsfm/tree/main/tsfm_public/models/tinytimemixer.
