The Improved Hybrid STD– Radial Basis Function Neural Network Approach for Time Series Forecasting Application to Tesla Stock Price Prediction

<ns3:p>Abstract* The forecast of time series in financial applications is difficult to perform as time series forecasting is nonlinear in nature, seasonal, and has structural variability. Stock price series tend to follow a lot of nonlinear dynamics, which undermines the power of single-model approaches. Hybrid decomposition-based models have attracted increasing interest in order to gain accuracy by separating heterogeneous features from one another. In this work, we present a hybrid forecasting methodology that incorporates STD decomposition with RBFNN (Radial Basis Function Neural Network). The time series is decomposed, where trend, seasonal, and dispersion components are separately modeled using RBFNN with Gaussian basis functions. The predicted feature sets are then recombined to construct a forecast, to be evaluated with weekly Tesla stock price data and standard accuracy performance metrics. The STD–RBFNN model gives very low forecasting errors under different variables and a high coefficient of determination. It shows superiority compared with an alternative hybrid neural network model, especially in modeling nonlinear variation under similar experimental conditions. This results in substantially greater forecasting accuracy because trend, seasonal, and dispersion components separate before neural modeling. The proposed STD and RBFNN pipeline is a good and highly flexible method to forecast complex nonlinear and seasonal financial time series.</ns3:p>