Attribution methods are essential for interpreting time series predictive models by quantifying the relevance of each time step for the prediction. State-of-the-art methods are often based on SHAP, an attribution method developed for tabular data. However, this has several challenges. First, SHAP is expensive to compute, especially for long time series, hence to speed it up it is usually approximated. Second, the impact of the background selection for emulating data ‘missingness’, essential to compute SHAP, remains understudied. Third, SHAP and more generally attribution methods for time series regression are notably lacking. In this paper, we address these limitations and propose TSHAP, a novel SHAP-based attribution method for time series classification and regression. TSHAP leverages a sliding window to group temporal data, enabling the efficient computation of exact SHAP values for each group. We further develop a methodology for the principled selection of background data. We evaluate TSHAP’s performance and robustness using comprehensive experiments on synthetic and real-world time series datasets.