This study highlights the introduction of Tiny Time Mixers (TTM), a compact AI model specifically designed for multivariate time series forecasting. Addressing the limitations of large pre-trained models that struggle with the diverse nature of time series data, TTM stands out with only 1M parameters while showcasing innovative techniques like adaptive patching and multi-level modeling. The model not only significantly boosts accuracy by up to 38% compared to conventional models, but it also dramatically reduces computational requirements, ultimately paving the way for more efficient forecasting in this domain.
The development of Tiny Time Mixers (TTM) addresses the shortcomings of large pre-trained models in multivariate time series forecasting by offering a compact and efficient solution.
Our Tiny Time Mixers demonstrate significant accuracy improvements, achieving 12-38% higher performance than existing benchmarks while being over 100 times smaller in parameters.
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