Taking into account the limitations that have been observed in LSTM methods, we propose a structure that addresses the shortcomings of LSTM models. To mitigate the forgetting phenomenon, a transformer-based (Vaswani et al. 2017) model is used. Transformer utilizes a matrix that incorporate all previous data in a sequence, determining correlated values of the data. Therefore, it does not suffer from forgetting, however, all data segments should be provided as input, confining the model to a specific attention window (this study, the historical days of the stock). The cornerstone and the key advantage of the transformer despite its memory size (n²) compared to LSTM (n.log(n)), is its ability for parallel computation. This capability has…