Forecasting Time Series Data Using Bayesian Regularization
Doni El Rezen Purba, Dewi Sartika Br Ginting · 2021
Forecasting or predicting future events is important to take into account in order for an activity to proceed properly. Flights predict the weather forecast, the banking industry predicts the price of currency, the health world predicts the disease, the retail business predicts total sales. prediction or forecasting of events is calculated using past data, usually in the form of time series. Artificial neural networks are capable of forecasting time-series data. Forecasting results with artificial neural network is influenced from the network architecture model is determined, one of which determination of training function. This study uses the bayesian regularization training function to forecast time clock data with several layer count models and the number of neurons.The results obtained with the number of 3 layers and each neuron of 36, 12, 6 for the best process performance, and the number of neurons 24, 12, 6 for the shortest iteration process.