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…
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