I am currently working on xAI techniques for TSC and TSF problems. As far as I remember, GradCAM is mostly suitable for CNN-based techniques, as it gives you a heatmap of which spatial features are contributing to the final decision and how much their contributions are. Also, if you consider using 1D CNN-based TSC algorithms, then they will give suboptimal results for longer sequences. So there is a huge trade-off while working on such an intersection of TSC and xAI, especially time and feature attribution (both).

My suggestion: Based on one of my recent works (currently under submission), you can explore GradCAM for smaller chunks (subsequences) of your time series data and then you can apply some basic counterfactual techniques by perturbing the highest contributing features through a guided optimization or adversarial learning.