TSFMAudit detects pretraining contamination in time series foundation models via probe adaptation dynamics (faster loss drop, smaller backbone shift), tested on 6 models and 187 datasets against 10 LLM-derived baselines.
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Tirex: Zero-shot forecasting across long and short horizons with enhanced in-context learning
17 Pith papers cite this work. Polarity classification is still indexing.
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Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
A model-agnostic adaptive conformal anomaly detection approach uses weighted quantile bounds learned from past foundation model predictions to deliver interpretable p-value scores with stable calibration under shifts for time series monitoring.
TempusBench is a new evaluation framework for time-series forecasting models that supplies fresh non-overlapping datasets, tasks beyond horizon and domain, consistent tuning across models, and visualization tools.
Flow matching on time series targets a closed-form nonparametric velocity field that is a similarity-weighted mixture of observed transition velocities, making neural models approximations to an ideal memory-augmented dynamical system sampler.
TS-Arena is a live pre-registration platform that evaluates time series forecasts on future data streams to eliminate information leakage.
AME-TS is a structure-guided sparse MoE foundation model for time series that aligns expert routing with series-level temporal descriptors to achieve strong accuracy-efficiency tradeoffs on GIFT-Eval while improving specialization stability.
RareCP improves interval efficiency for time series conformal prediction by retrieving and weighting regime-specific calibration examples while adapting to drift and maintaining coverage.
Foundation models outperform dataset-specific machine learning in energy time series forecasting across 54 datasets in 9 categories.
iAmTime is a time-series foundation model that uses instruction-conditioned in-context learning from demonstrations to perform zero-shot adaptation on forecasting, imputation, classification, and related tasks.
Timer-S1 is a released 8.3B-parameter MoE time series model that achieves state-of-the-art MASE and CRPS scores on GIFT-Eval using serial scaling and Serial-Token Prediction.
X-TRACK is the first xLSTM model with explicit kinematic constraints that generates realistic highway trajectories and outperforms baselines on highD while matching SOTA on NGSIM.
Kairos is a parameter-efficient time series foundation model using dynamic patching tokenizer, mixture-of-size encoding, and spectral-conditioned positional embeddings to improve zero-shot forecasting on heterogeneous data.
NormWear-2 encodes physiological signals and interventions into a shared latent space, models their joint evolution as a dynamical system, and uses chaos-theoretic balancing during pretraining to achieve superior multi-scale forecasting on diverse real-world datasets.
Embedding spaces of time series foundation models make mean shifts, variance changes, and trends linearly detectable, but detection degrades smoothly with shift strength and shows model-specific failure modes.
AI offers opportunities to advance fusion energy R&D but requires responsible practices and expert collaborations to overcome its inherent challenges.
citing papers explorer
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TS-Arena -- A Live Forecast Pre-Registration Platform
TS-Arena is a live pre-registration platform that evaluates time series forecasts on future data streams to eliminate information leakage.
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X-TRACK: Physics-Aware xLSTM for Realistic Vehicle Trajectory Prediction
X-TRACK is the first xLSTM model with explicit kinematic constraints that generates realistic highway trajectories and outperforms baselines on highD while matching SOTA on NGSIM.
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Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models
Kairos is a parameter-efficient time series foundation model using dynamic patching tokenizer, mixture-of-size encoding, and spectral-conditioned positional embeddings to improve zero-shot forecasting on heterogeneous data.