MarsTSC is a VLM-based agentic reasoning framework with a self-evolving knowledge bank and Generator-Reflector-Modifier roles that achieves better few-shot multimodal time series classification than baselines on 12 benchmarks.
Moirai-MoE: Empowering time series foundation models with sparse mixture of experts
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6verdicts
UNVERDICTED 6representative citing papers
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.
FeDPM learns and aligns local discrete prototypical memories across domains to create a unified discrete latent space for LLM-based time series foundation models in a federated setting.
A new MoE training method integrates expert-level losses and partial online updates to improve forecasting accuracy and efficiency over standard statistical and neural models.
Sonata is a small hybrid world model pre-trained to predict future IMU states that outperforms autoregressive baselines on clinical discrimination, fall-risk prediction, and cross-cohort transfer while fitting on-device wearables.
The paper benchmarks foundation models like TimesFM and Chronos against baselines on eight forecasting capabilities for power system time series.
citing papers explorer
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Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning
MarsTSC is a VLM-based agentic reasoning framework with a self-evolving knowledge bank and Generator-Reflector-Modifier roles that achieves better few-shot multimodal time series classification than baselines on 12 benchmarks.
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TempusBench: An Evaluation Framework for Time-Series Forecasting
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.
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Discrete Prototypical Memories for Federated Time Series Foundation Models
FeDPM learns and aligns local discrete prototypical memories across domains to create a unified discrete latent space for LLM-based time series foundation models in a federated setting.
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Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration
A new MoE training method integrates expert-level losses and partial online updates to improve forecasting accuracy and efficiency over standard statistical and neural models.
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Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity
Sonata is a small hybrid world model pre-trained to predict future IMU states that outperforms autoregressive baselines on clinical discrimination, fall-risk prediction, and cross-cohort transfer while fitting on-device wearables.
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Empirical Assessment of Time-Series Foundation Models For Power System Forecasting Applications
The paper benchmarks foundation models like TimesFM and Chronos against baselines on eight forecasting capabilities for power system time series.