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The number of electric vehicles (EV) has increased significantly in the past decades due to its advantages including emission reduction and improved energy efficiency. However, the adoption of EV could lead to overloading the grid and degrading the power quality of the distribution system. It also demands an increase in the number of EV charging stations. To meet the charging needs of 15 million EVs by the year 2030 with limited charging stations, prediction of charging needs, and reallocating charging resources are in emerging needs. In this study, long short-term memory (LSTM) and autoregressive and moving average models (ARMA) models were applied to predict charging loads with temporal profiles from 3 charging stations. Prediction accuracy was applied to evaluate the performance of the models. The LSTM models demonstrated a significant performance improvement compared to ARMA models. The results from this study lay a foundation to efficiently manage charge resources.