Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support forecasting and transferable representation learning, classification still typically requires fitting a task-specific classifier on each target dataset, while individual ch...
Jun Fang, Shi-Feng Xie, Rui-Chu Cai et al.· 0 citations
PaCTS is introduced, which generates a small set of instance-adaptive latent prompts in the form of continuous embedding tokens conditioned on the visible context, which serve as compact context surrogates for frozen TSFMs.
Ze-Hao Xiao, Shi-Feng Xie, Lei Zan et al.· 0 citations
This work analyzes TSFM post-training methods based on their locus of intervention in the prediction pipeline, yielding five categories: parameter adaptation, context augmentation, model composition, output processing and uncertainty control, and compression and specialization.
Tabby, a long context probabilistic time series foundation model, is released together with a complete and open recipe of how it was built, which achieves competitive zero-shot forecasting performance on GIFT-Eval and the out-of-distribution TIME benchmark.
Shi-Feng Xie, Bahaeddine Abdessalem, Ze-Hao Xiao et al.· 1 citation
FlowTSFM is introduced, an encoder architecture that interprets depth as a recurrent transport process: a single Transformer block is iteratively applied with shared parameters, while a quantile-flow objective supervises intermediate states along a prescribed trajectory from a prior distribution toward the final foreca...
Bahaeddine Abdessalem, Shi-Feng Xie, Ze-Hao Xiao et al.· 0 citations
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