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Time series data analysis is crucial in various fields, but as data complexity increases, traditional analysis methods struggle to clarify temporal patterns. While AI models offer high accuracy, their black-box nature limits trust in critical decision-making. Explainable AI (XAI) helps by visually illustrating which variables influence predictions, improving transparency. However, existing XAI models, designed for image analysis, are inadequate for Point-level analysis in multivariate time series data. To address this, we propose Cross-GradCAM that provides Point-level explanations using a deep neural network with a specialized 1D convolution. This model precisely identifies Point-Level features at the intersection of time and variables. Experimental results highlight the efficacy of Cross-GradCAM, with the model achieving an accuracy of 99.2%, recall of 87.6%, and F1-score of 83.8%, significantly outperforming existing methods. [ABSTRACT FROM AUTHOR]