flowstate 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.

Update: Changes from 1.0 to 1.1

• Inclusion of synthetic pre-training data following CauKer
• Increaded pre-training context 2048 -> 4096
• Improved S5 Layer with an output gating mechanism
• Optimized hyperparameters
• Larger MLP layer
• Total number of parameters: 18.5M

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.

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:

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