ARTIFICIAL NEURAL NETWORK ALGORITHM EVALUATION, MULTIPLE REGRESSION ANALYSIS AND SUPPORT VECTOR MACHINE ON STOCK MARKET PREDICTION - johar, ade (Lembaga Penelitian Universitas Swadaya Gunung Jati, 2023)
Financial time series is one of the most challenging applications of modern time series forecasting. Financial time series is closely related to noise (disturbance signals), non-stationary and deterministic chaotic. The characteristics indicate that no exhaustive information can be obtained from the past behavior of financial markets to capture the full dependency between future prices and that of the past. The purpose of this study is to determine the correct algorithm for Artificial Neural Networks, Multiple Regression Analysis and Support Vector Machines so that the highest accuracy value for prediction trend accuracy and the smallest value for Root Mean Square Error can be obtained. The research method used is 1. Neural network, 2. SVM, 3. Linear regression. From the data that has been tested using the Rapidminer tool with parameters that refer to several literatures [5], data collection is taken from the Stock Market Online Application