A Comparison between ARIMA, LSTM, and GRU for Time Series Forecasting

A critical area of machine learning is Time Series forecasting, as various forecasting problems contain a time component. A series of observations taken chronologically in time is known as a Time Series. In this research, however, we aim to compare three different machine learning models in making a time series forecast. We are going to use the Bitcoin's price dataset as our time series data set and make predictions accordingly. The results show that the ARIMA model gave better results than the deep learning-based regression models. ARIMA gives the best results at 2.76% and 302.53 for MAPE and RMSE respectively. The Gated Recurrent Unit (GRU) however performed better than the Long Short-term Memory (LSTM), with 3.97% and 381.34 of MAPE and RMSE respectively.