A survey proposing a taxonomy of Injective, Bridging, and Internal Alignment paradigms to evolve TSA into user-driven Time Series Question Answering with LLMs.
Lstprompt: Large language models as zero-shot time series forecasters by long-short-term prompting
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A two-stage SFT-plus-RL framework gives a 7B language model step-by-step time series reasoning and beats or matches specialized forecasters on most of nine datasets.
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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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Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
A two-stage SFT-plus-RL framework gives a 7B language model step-by-step time series reasoning and beats or matches specialized forecasters on most of nine datasets.