This survey and benchmark of deep time series models using the released TSLib library finds that models with specific structures perform well only on distinct analysis tasks.
Unitime: A language-empowered unified model for cross-domain time series forecasting
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A vision-language LLM forecasts HAP attitudes from telemetry for proactive beamforming, achieving 22.1% higher user service ratio and 12.5% higher sum-rate than baselines in simulations with mean latency of 36 ms.
A survey proposing a taxonomy of Injective, Bridging, and Internal Alignment paradigms to evolve TSA into user-driven Time Series Question Answering with LLMs.
Zeus proposes a multi-scale Transformer with point-wise tokenization and Multi-Objective Temporal Masking to enable tuning-free performance on forecasting, interpolation, and other time series tasks.
citing papers explorer
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Deep Time Series Models: A Comprehensive Survey and Benchmark
This survey and benchmark of deep time series models using the released TSLib library finds that models with specific structures perform well only on distinct analysis tasks.
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Multimodal Large Language Model Enabled Robust Beamforming for HAP Downlink Communications
A vision-language LLM forecasts HAP attitudes from telemetry for proactive beamforming, achieving 22.1% higher user service ratio and 12.5% higher sum-rate than baselines in simulations with mean latency of 36 ms.
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From Time Series Analysis to Question Answering: A Survey in the LLM Era
A survey proposing a taxonomy of Injective, Bridging, and Internal Alignment paradigms to evolve TSA into user-driven Time Series Question Answering with LLMs.
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Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis
Zeus proposes a multi-scale Transformer with point-wise tokenization and Multi-Objective Temporal Masking to enable tuning-free performance on forecasting, interpolation, and other time series tasks.