Forecasting time series is a classical challenge in time series analysis. Applying machine learning based forecasting on the biomedical electrocardiogram (ECG) signals is not common but vital in predicting the overall health assessment of an individual. In this study, we forecast ECG signals using temporal fusion transformers (TFT) for the very first time. We use the renowned publicly available dataset, PTB-XL, to forecast all of the 12 leads. Different hyper parameters are used with temporal fusion transformer obtain optimal results. We downsample and normalize our dataset in order to reduce computational complexity as part of pre processing. Then it is fed into a TFT with different hyperparameter settings. We get an average root mean square error of 0.061 and mean absolute error of 0.128. The promising results encourage the application of TFTs for forecasting of ECG signals specifically for a better explainability emphasizing the research in explainable artificial intelligence (XAI).