Rice Price Forecasting Using GridSearchCVand LSTM

Forecasting supports the decision-making process. Many business decisions affect the entire line of companies or individuals. Therefore, forecasting needs to be supported by the most effective methods. Rice as a food staple is the most important part for the community and business actors. Forecasting the price of rice will certainly support the right decision making for the community or business actors. The purpose of this study is to present a rice price forecasting model that is effective in providing input to the public. This effective output will be built using LSTM supported with GridSearchCV and Sliding Window. The datasets are taken from the Central Statistics Agency which records rice price movements over a period of several years. This research procedure begins with data collection, data preparation, model development, model evaluation and deployment into rice price forecasting applications. The results of this study indicate that the deployed application can predict prices well and support the community and business actors in making decisions.