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granite-timeseries-flowstate-r1

ibm-granite · granite-timeseries-flowstate-r1

**Important**: To use FlowState-r1.1 include the `revision=r1.1` argument when loading the model, as demonstrated in the `Recommended Use` section. If no `revision` argument is provided, the version 1.0 is used. - **FlowState**: We present an SSM-based time series foundation model that can be dynamically adjusted to the specific characteristics of the time series during evaluation. - **Functional Basis Decoder (FBD)**: We propose a novel decoder, as a critical component of FlowState, that utilizes a set of continuous basis functions to make continuous forecasts and allow seamless adjustment to specific input characteristics. - **Flexible temporal adaptation**: FlowState can dynamically adjust the context and target length to the timescale of the provided time series. - **Compact and high-performing**: With fewer than 10M parameters and the ability to forecast multiple consecutive patches in parallel, FlowState delivers state-of-the-art accuracy with exceptional efficiency. This model card contains the model weights for the Granite version of the FlowState model, which is suitable for commercial and enterprise use. A research version of the model (available here) is also provided and is intended for research use only. The Granite and research versions achieve the same MASE performance on GIFT‑Eval, indicating comparable point forecasting accuracy. The research version demonstrates slightly improved CRPS performance, which evaluates the quality of probabilistic forecasts. Despite being **more than 10x smaller** than the next best models, FlowState is **better than all Zero-Shot models** with publicly available source code and model weights on the GIFT-Eval Leaderboard in terms of MASE performance, as of Apr. 28th 2026. The Figure compares GIFT MASE Performance vs. model size for FlowState and the most competitive Zero-Shot Models.

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표 및 시계열 예측license:apache-2.0
컨텍스트 창미공개
입력 → 출력待核验 → 待核验
모델 웨이트미확인
정보 업데이트2026/11/2 19:16:04
OVERVIEW

모델 소개

**Important**: To use FlowState-r1.1 include the `revision=r1.1` argument when loading the model, as demonstrated in the `Recommended Use` section. If no `revision` argument is provided, the version 1.0 is used. - **FlowState**: We present an SSM-based time series foundation model that can be dynamically adjusted to the specific characteristics of the time series during evaluation. - **Functional Basis Decoder (FBD)**: We propose a novel decoder, as a critical component of FlowState, that utilizes a set of continuous basis functions to make continuous forecasts and allow seamless adjustment to specific input characteristics. - **Flexible temporal adaptation**: FlowState can dynamically adjust the context and target length to the timescale of the provided time series. - **Compact and high-performing**: With fewer than 10M parameters and the ability to forecast multiple consecutive patches in parallel, FlowState delivers state-of-the-art accuracy with exceptional efficiency. This model card contains the model weights for the Granite version of the FlowState model, which is suitable for commercial and enterprise use. A research version of the model (available here) is also provided and is intended for research use only. The Granite and research versions achieve the same MASE performance on GIFT‑Eval, indicating comparable point forecasting accuracy. The research version demonstrates slightly improved CRPS performance, which evaluates the quality of probabilistic forecasts. Despite being **more than 10x smaller** than the next best models, FlowState is **better than all Zero-Shot models** with publicly available source code and model weights on the GIFT-Eval Leaderboard in terms of MASE performance, as of Apr. 28th 2026. The Figure compares GIFT MASE Performance vs. model size for FlowState and the most competitive Zero-Shot Models.

주요 특징

特点摘要待依据来源补齐,不凭模型名称推断能力。

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许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。