Granite Time Series

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Overview

Flowstate, Tiny Time Mixer (TTM), and Time Series Pulse (TSPulse) are IBM’s family of ultra-lightweight pre-trained models for time series data, with just a few million parameters and GPU-free inference. Despite their compact size, these models deliver strong performance across forecasting, classification, anomaly detection, imputation, and similarity search tasks. FlowState is a time-scale adjustable foundation model, designed to offer flexible and efficient forecasting across various temporal scales. By integrating a State Space Model (SSM) Encoder with a Functional Basis Decoder, FlowState transitions into a timescale-invariant coefficient space, enabling continuous forecasting adaptable to any sampling rate. This architecture allows for training at one timescale and inference at another, significantly enhancing the utility of training data across diverse temporal structures. FlowState delivers state-of-the-art accuracy in zero-shot forecasting tasks, holding the #3 spot on the GIFT-Eval leaderboard (as of 10/2/2025). TTM models supports multivariate forecasting via both channel independence and channel-mixing approaches. Decoder Channel-Mixing can be enabled during fine-tuning for capturing strong channel-correlation patterns across time-series variates, a critical capability lacking in other existing approaches. In addition, TTM also supports exogenous and categorical data infusion. TSPulse introduces a novel dual-space masked reconstruction that jointly learns from time and frequency domains, unifying complementary signals into a shared embedding space. A dual-embedding disentanglement mechanism separates fine-grained and semantic features, while task-relevant heads are prioritized during pretraining through reweighted loss objectives. This produces task-specific refined representations, enabling efficient transfer learning for downstream tasks.

Model cards

Granite Flowstate r1

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