The introduction of Attention Long Short Term Memory (ALSTM) produces an alternative to Long Short Term Memory (LSTM) by aiming to optimize information passing via removing the complexity of the cells in LSTM. In this work, the results and comparison of the performance of LSTM algorithms versus ALSTM architectures are assessed through the forecast of airline passenger numbers over months. The results are analyzed through qualitative and quantitative data. The hypothesis made in this paper is that ALSTM will perform better than the LSTM, and the results show that the hypothesis was correct under some circumstances, although by a small margin.