Time Series Analysis and Forecast Accuracy Comparison of Models Using RMSE–Artificial Neural Networks

Abstract

Primary importance of our research paper is to demonstrate the time series analysis and forecast accuracy of different selected models based on neural networks. Fundamentally important to many practical applications is time series modeling and forecasting. As a result, there have been numerous ongoing research projects on this topic for many months. For enhancing the precision and efficacy of time series modeling and forecasting, numerous significant models have been put out in the literature. The purpose of this research is to give a brief overview of some common time series forecasting methods that are implemented, along with their key characteristics. The most frugal model is chosen with great care when fitting one to a data set of Pune precipitation data from 1965 to 2002. We have utilized the RMSE (root mean square error) as a performance index to assess forecast accuracy and to contrast several models that have been fitted to a time series. We applied feed-forward, time-lagged, seasonal neural networks, and long short-term memory models on selected dataset. The long short-term memory neural model worked better than other models.