Time Series Foundation Models
PulseAugur coverage of Time Series Foundation Models — every cluster mentioning Time Series Foundation Models across labs, papers, and developer communities, ranked by signal.
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Time Series Models Evaluated for US Influenza Forecasting
A new research paper evaluates various time series forecasting models for predicting seasonal influenza in the United States. The study found that a mixture-of-experts model, which combines multiple pretrained forecaste…
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New framework distills foundation models for specialized time-series forecasting
Researchers have developed a novel framework called Guard to distill knowledge from large, general-purpose foundation models (FMs) into lightweight, specialized time-series forecasters. This approach addresses the chall…
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Time series model benchmarks may hide critical failures, study finds
A new research paper published on arXiv highlights potential shortcomings in current benchmarks for time series foundation models (TSFMs). The study, focusing on traffic speed forecasting, reveals that aggregate metrics…
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NYC congestion pricing boosts transit use, study finds
A new study published on arXiv utilizes time series foundation models to analyze the impact of New York City's congestion pricing program, implemented in January 2025. The research found that bus and subway ridership in…
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New TS-Memory Adapter Enhances Time Series Foundation Models
Researchers have developed TS-Memory, a novel plug-and-play memory adapter designed to enhance Time Series Foundation Models (TSFMs). This method addresses the challenges of adapting TSFMs to new domains by mitigating c…
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Vision-Language Models Serve as Judges for Time Series Forecasting
Researchers have introduced TimeVista, a new framework that utilizes Vision-Language Models (VLMs) to evaluate time series forecasting. This approach leverages VLMs' ability to interpret time series plots alongside text…
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New ORCA Method Adapts Time Series Models in Black-Box Settings
Researchers have developed ORCA (Online Residual Contextual Adaptation), a novel method for adapting Time Series Foundation Models (TSFMs) in a black-box setting. This approach focuses on learning from the predictive er…
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New Agentic Framework Enhances Time Series Forecasting with LLMs
Researchers have introduced KairosAgent, a new framework designed to improve multimodal time series forecasting. This agentic system combines a Large Language Model (LLM) for semantic reasoning with a Time Series Founda…
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New method audits time series foundation models for data contamination
Researchers have introduced TSFMAudit, a novel method designed to detect data contamination in time series foundation models (TSFMs). This is the first study to address pretraining contamination auditing specifically fo…
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New AI methods enhance time series forecasting accuracy and interpretability
Researchers have introduced several new methods for time-series forecasting, aiming to improve accuracy and generalization. MeLISA, a latent-free autoregressive model, enhances rollout efficiency and long-horizon statis…
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TS-Arena platform enables live forecasting evaluation with pre-registration protocol
Researchers have developed TS-Arena, a novel platform designed to rigorously evaluate Time Series Foundation Models (TSFMs) by testing them on future, unknown data. This live forecasting system enforces a strict pre-reg…