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flowstate

ibm-research · flowstate

**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 research-use only and full reproducibility of our results published in our paper. However - if you are looking for the FlowState model weights for commercial and enterprise use, please refer to our granite releases here Despite being **more than 10x smaller** than the next best models, FlowState is the **best Zero-Shot model** with publicly available source code and model weights on the GIFT-Eval Leaderboard, as of Mar. 16th 2026. The Figure compares GIFT MASE Performance vs. model size for FlowState and the most competitive Zero-Shot Models. Model Details can be found in our Paper. Currently FlowState only supports zero-shot forecasting. FlowState can be used to make predictions as follows:

번역 대기 중입니다. 원문을 표시합니다.

표 및 시계열 예측license:apache-2.0
컨텍스트 창미공개
입력 → 출력待核验 → 待核验
모델 웨이트미확인
정보 업데이트2026/10/30 20:56:08
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 research-use only and full reproducibility of our results published in our paper. However - if you are looking for the FlowState model weights for commercial and enterprise use, please refer to our granite releases here Despite being **more than 10x smaller** than the next best models, FlowState is the **best Zero-Shot model** with publicly available source code and model weights on the GIFT-Eval Leaderboard, as of Mar. 16th 2026. The Figure compares GIFT MASE Performance vs. model size for FlowState and the most competitive Zero-Shot Models. Model Details can be found in our Paper. Currently FlowState only supports zero-shot forecasting. FlowState can be used to make predictions as follows:

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。