<ul><li>This news article discusses a benchmark study that evaluates the effectiveness of different reasoning strategies for zero-shot time-series forecasting.</li><li>The study focuses on understanding the applicability and impact of reasoning strategies in zero-shot time-series forecasting, specifically in the context of challenging tasks.</li><li>The benchmark, called ReC4TS, conducts comprehensive evaluations across datasets in eight domains and covers both unimodal and multimodal forecasting tasks.</li><li>Insights from the study suggest that self-consistency is the most effective test-time reasoning strategy, and multimodal time-series forecasting benefits more from reasoning strategies compared to unimodal forecasting.</li></ul>

Evaluating System 1 vs. 2 Reasoning Approaches for Zero-Shot Time-Series Forecasting: A Benchmark and Insights

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