English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Large language models (LLMs), with demonstrated reasoning abilities acrossmultiple domains, are largely underexplored for time-series reasoning (TsR),which is ubiquitous in the real world. In this work, we propose TimerBed, thefirst comprehensive testbed for evaluating LLMs' TsR performance. Specifically,TimerBed includes stratified reasoning patterns with real-world tasks,comprehensive combinations of LLMs and reasoning strategies, and varioussupervised models as comparison anchors. We perform extensive experiments withTimerBed, test multiple current beliefs, and verify the initial failures ofLLMs in TsR, evidenced by the ineffectiveness of zero shot (ZST) andperformance degradation of few shot in-context learning (ICL). Further, weidentify one possible root cause: the numerical modeling of data. To addressthis, we propose a prompt-based solution VL-Time, using visualization-modeleddata and language-guided reasoning. Experimental results demonstrate thatVl-Time enables multimodal LLMs to be non-trivial ZST and powerful ICLreasoners for time series, achieving about 140% average performance improvementand 99% average token costs reduction.

A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization