A Review on the Comparison of Box Jenkins ARIMA and LSTM of Deep Learning

A time series is a set of events, sequentially calculated over time. Predicting the Time Series is mostly about predicting the future. The ability of a time series forecasting model is determined by its success in predicting the future. This is often at the expense of being able to explain why a specific prediction was made. The Box Jenkins model implies that the time series is stationary, and thus suggests differentiating non stationary series once or several times to obtain stationary effects. This generates a model for ARIMA, the I being the word for Integrated . The LSTM networks, comparable to computer memory, enforce a gated cell for storing information. Such as the previously mentioned networks, the LSTM cells also recognize when to make preceding time steps reads and writes information. Even though the work is new, it is obvious that LSTM architectures provide tremendous prospects as contenders for modeling and forecasting time series. The outcomes of the overall discrepancy in error indicate that in regards of both RMSE and MAE, the LSTM model tended to have greater predictive accuracy than the ARIMA model. Stavelin Abhinandithe K | Madhu B | Balasubramanian S | Sahana C "A Review on the Comparison of Box-Jenkins ARIMA and LSTM of Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39831.pdf Paper URL: https://www.ijtsrd.com/other-scientific-research-area/applied-mathamatics/39831/a-review-on-the-comparison-of-boxjenkins-arima-and-lstm-of-deep-learning/stavelin-abhinandithe-k