SpecReTF improves time series forecasting by retrieving similar historical patterns using windowed frequency representations with combined amplitude-phase similarity and exponential recency weighting, outperforming time-domain methods on benchmarks.
InProceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining, pages 5351–5362
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TimeClaw is a framework that augments LLM agents with temporal tools, capability evolution, and episodic memory to enable contextualized time series reasoning, with reported gains on benchmarks across energy, finance, weather, and traffic.
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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Spectral Retrieval-Augmented Time-Series Forecasting
SpecReTF improves time series forecasting by retrieving similar historical patterns using windowed frequency representations with combined amplitude-phase similarity and exponential recency weighting, outperforming time-domain methods on benchmarks.
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Harnessing Generalist Agents for Contextualized Time Series
TimeClaw is a framework that augments LLM agents with temporal tools, capability evolution, and episodic memory to enable contextualized time series reasoning, with reported gains on benchmarks across energy, finance, weather, and traffic.