Comparative analysis of stock price ARIMA and LSTM forecasting methods - Bielskis, Aivaras; Belovas, Igoris (Lietuvos matematikos rinkinys, 2022)
In the work, relevant methods of stock price forecasting are applied and compared: statistical time series (ARIMA, SARIMA) and neural network-based (LSTM). The results of stock price (Amazon, Apple, Google, Netflix, and Tesla companies) simulations are evaluated using MAE and MRE measures. The conclusions obtained in the work made it possible to identify shortcomings of the approaches and specify guidelines for improvements and further research.
Keywords: time series; neural networks; forecasting; ARIMA; SARIMA; LSTM