Despite having fewer than 1M parameters, the IBM Granite TimeSeries TTM outperforms larger models requiring billions of parameters in both zero-shot and few-shot forecasting tasks. TTM supports point forecasting across minutely to hourly intervals and is optimized for efficiency, allowing fine-tuning and inference on a single GPU or even a laptop. Its compact yet powerful design makes it ideal for scalable, real-world time-series applications.

The IBM Granite TimeSeries TTM model is developed following IBM's AI Ethics principles, leveraging high-quality public time-series datasets with diverse augmentations to enhance forecasting accuracy. It is designed for accessibility and efficiency, enabling responsible AI use in time-series applications. Released under the Apache 2.0 license, TTM is available for both research and commercial use.

The IBM Granite TimeSeries TTM model is designed for multivariate time-series forecasting across various domains. It supports point forecasting at different time resolutions, from minutely to hourly intervals, making it adaptable to a wide range of real-world applications. With strong zero-shot and fine-tuning capabilities, TTM enables businesses and researchers to develop precise, efficient forecasting models without requiring extensive computational resources.