WEATHER FORECAST FROM TIME SERIES DATA USING LSTM ALGORITHM

Abstract

Accurate weather forecasts play an important role in today's world as various sectors such as marine, navigation, agriculture and industry are basically dependent on weather conditions. Weather forecasts are also used to predict the occurrence of natural disasters. Weather forecasting determines the exact value of weather parameters and then predicts future weather conditions. In this study the parameters used are. Different weather parameters were collected from the Serang Maritime Meteorological Station and then analyzed using a neural network-based algorithm, namely Long-short term memory (LSTM). In predicting future weather conditions using LSTM neural networks are trained using a combination of different weather parameters, the weather parameters used are temperature, humidity, rainfall, and wind speed. After training the LSTM model using these parameters, future weather predictions are performed. The prediction results are then evaluated using RMSE. Prediction results show that the model is more accurate when predicting temperature data with RMSE 0.37, then RMSE wind speed 0.72, RMSE sunlight 2.79, and RMSE humidity 5.05. This means that the model is very good at studying weather data, inversely proportional to humidity data.