Abstract
This manuscript aims to study and compare the Long Short-Term Memory (LSTM) Deep learning to Auto regressive Integrated Moving Average (ARIMA) algorithms for a univariate time series, especially for stock price series. Using the mean absolute percentage error, the mean absolute error, or either root-mean-square deviation and according to our extracted dataset, we find that the classical approaches like ARIMA out-perform deep learning ones since they are very simple to use especially for linear univariate datasets. More specifically, LSTM deep learning algorithms are more powerful and provide better results in terms of predictions.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
