Rice plays a crucial role globally, as it is widely consumed across nations. Therefore, studying rice prices is vital, since fluctuations in the price can affect both its consumption and availability. This study analyzes time-series models using an international dataset. After preprocessing, the dataset comprises 71,856 samples and eight input features from six countries. The original dataset contained 300,816 rows and 23 columns. This study aims to predict rice inflation rates using time series models such as ARIMA, LSTM, and BiLSTM. The ARIMA model achieved the best combination of values (4,1,4)(0,0,0). Various statistical techniques that calculate inflation rates require expert knowledge... More >