LSTM with Bayesian Slide Optimization for Time Series Forecasting in Real Time

All across the world, forecasting the weather is a difficult task. Some of the factors that are utilized to anticipate the weather are the movement of air and clouds in the sky as well as historical weather patterns, changes in air pressure as well as the usage of computer models. Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) have all been proposed by researchers. This study uses a sliding window approach to make predictions. In order to use a neural network, the first step is to format and prepare the data for use. Using a variety of windows, the second phase computes the results month by month, week by week, and even day by day in order to ensure the findings are accurate. Before finalization, hyperparameter tuning is utilized to select the best window for enhanced prediction. According to the study’s findings, the method used to predict weather patterns looks to be extremely successful.