Optimizing Neural Network Performance for Time Series Analysis: Techniques for Improving Output Accuracy

The purpose of this study is to investigate the effectiveness of increasing the number of nodes in the input layer of an artificial neural network (ANN) to improve its output and achieve desired results. The input stage of a neural network is crucial in enabling it to learn and adapt to any given model. However, at times, the neural network may not reach its target due to incorrect identification of the input variables. The study found that adding nodes to the input layer boosts neural network output and helps achieve the desired outcomes. The study advances artificial neural network research and improves machine learning model accuracy. A future study investigating improved output neural networks would be very interesting.