Granite TimeSeries TTM R2 Model Card TinyTimeMixers (TTMs) are compact pre trained models for Multivariate Time Series Forecasting, open sourced by IBM Research. With model sizes starting from 1M params, TTM introduces the notion of the first ever “tiny” pre trained models for Time Series Forecasting. The paper describing TTM was accepted at NeurIPS 24. TTM outperforms other models demanding billions of parameters in several popular zero shot and few shot forecasting benchmarks. TTMs are lightweight forecasters, pre trained on publicly available time series data with various augmentations. TTM provides state of the art zero shot forecasts and can easily be fine tuned for multi variate forecasts with just 5% of the training data to be competitive. Note that zeroshot, fine tuning and inference tasks using TTM can easily be executed on 1 GPU or on laptops. TTM r2 comprises TTM variants pre trained on larger pretraining datasets (\~700M samples). The TTM r2.1 release increases the pretraining dataset size to approximately (\~1B samples). The prior model releases, TTM r1, were trained on \~250M samples and can be accessed here. In general, TTM r2 models perform better than TTM r1 models…
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