granite timeseries flowstate r1 EU AI Act Compliance Profile

FlowState

Paper | HuggingFace Model Card | GitHub Model Code

FlowState is the first time-scale adjustable Time Series Foundation Model (TSFM), open-sourced by IBM Research. Combining an State Space Model (SSM) Encoder with a Functional Basis Decoder allows FlowState to transition into a timescale invariant coefficient space and make a continuous forecast from this space. This allows FlowState to seamlessly adjust to all possible sampling rates. Therefore, training in one time-scale helps for inference at all scales, allowing for drastically improved utilization of training data across time-scales. This innovation leads to a significant improvement in performance, making FlowState the new state-of-the art in zero-shot time series forecasting.

Key Features

• 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.

Benchmark Highlights

Despite being more than 10x smaller than the 3 next best models, FlowState is the best Zero-Shot model on the [GIFT-Ev

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