Recent explainable AI (XAI) methods for time series primarily estimate pointwise attribution magnitudes, while overlooking the directional (positive/negative) impact on model predictions, leading to suboptimal identification of significant points. Concerning this, our analysis demonstrates that conventional Integrated Gradients (IG) effectively capture critical points that have both positive and negative impacts on model predictions. However, current evaluation metrics fail to properly assess this capability, as they inadvertently cancel out opposing feature contributions. To address this limitation, we propose novel evaluation metrics—Cumulative Prediction Difference (CPD) and Cumulative Prediction Preservation (CPP)—designed to systematically assess whether attribution methods accurately identify significant positive and negative points in time series XAI. Under these metrics, we discover that conventional integrated gradients (IG) outperform their recent counterparts. However, directly applying IG to time series data may lead to suboptimal outcomes, as the generated paths do not take temporal relationships into account and introduce out-of-distribution samples. To overcome these challenges, we introduce Timing, which enhances IG by incorporating temporal awareness while maintaining its theoretical properties.Extensive experiments on synthetic and real-world time series benchmarks demonstrate that Timing outperforms existing time series XAI baselines.