Abstract
Statistical methods and artificial intelligence play an important role in building models that can predict time series and thus help in developing strategic plans. The study aimed to propose a hybrid model (ARIMA and artificial neural networks) to predict the temperature in the city of Baghdad and compare it with the AA and N models based on the temperatures in the city of Baghdad for the period from January 2017 to December 2021. By comparing these models using forecast accuracy measures MAE, MSE, and MAPE, it was found that the hybrid method gives lower error values than ARIMA models and artificial neural networks, as it addresses the problem of non-linearity in the data, so it was relied upon to predict temperatures in the city of Baghdad for the next six months.
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 callMore From: International Journal of Computer Science and Mobile Computing
Disclaimer: 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.
