Large language models (LLMs) are increasingly used to explain numerical time series, but fluent explanations can misstate when a change occurs, overstate a trend, or introduce unsupported patterns. Existing evaluations often score forecasts, answers, or whole explanations, leaving claim-level temporal grounding undermeasured. We introduce TimeClaimBench, a benchmark that parses explanations into atomic temporal claims and verifies them against a deterministic extracted event graph. Across five LLMs on controlled synthetic series, event-grounded prompting raises the strict graph-supported rate by 18.3 points and reduces graph-unsupported claims by 27.9 points under this fixed operational criterion. Calibration analyses show the boundary of that result. The extractor recovers the designed primary event in 394 of 400 series, but this does not estimate the precision of additional extracted events. A blinded two-annotator comparison on one model and 40 series reverses the automatic ordering: holistic ratings favor Direct and chain-of-thought outputs over event-grounded outputs. therefore distinguishes four axes that should not be conflated: designed-event recovery, the unresolved precision of additional events, claim-to-graph consistency, and whole-explanation human judgment. Code: https://github.com/lunaaa95/TimeClaimBench

Aspect-based sentiment analysis (ABSA) has drawn more and more attention because of its extensive applications. However, towards the sentence carried with more than one aspect, most existing works generate an aspect-specific sentence representation for each aspect term to predict sentiment polarity, which neglects the sentiment relationship among aspect terms. Besides, most current ABSA methods focus on sentences containing only one aspect term or multiple aspect terms with the same sentiment polarity, which makes ABSA degenerate into sentence-level sentiment analysis. In this paper, to deal with this problem, we construct a heterogeneous graph to model inter-aspect relationships and aspect-context relationships simultaneously and propose a novel Composition-based Heterogeneous Graph Multi-channel Attention Network (CHGMAN) to encode the constructed heterogeneous graph. Meanwhile, we conduct extensive experiments on three datasets: MAMSATSA, Rest14, and Laptop14, experimental results show the effectiveness of our method.