Semiconductor Price Index Predicting Based on a Novel Improved AdaBoost Feature-Weighted Combination Model

Abstract The semiconductor price index serves as a vital metric for assessing technological developments and related market trends. Establishing a more accurate forecasting model for the semiconductor price index is of significant importance for analyzing the industry’s trends and market directions. In this paper, a novel framework for semiconductor price index forecasting is proposed. In addition to traditional financial data, the study introduces search engine data (Google Trends) representing investor attention, and introduces text information extracted from online news headlines reflecting major market events and government policies as independent variables. Used to predict the dependent variable: The PHLX Semiconductor Sector (SOX). First, the XGBoost model is employed to compute the importance scores of each feature. Then, a feature weight coefficient indicator is constructed based on these importance scores to calculate the weight coefficient indicator values for each feature. These indicator values are then used to weight the kernel function of Support Vector Regression (SVR), resulting in weighted Support Vector Regression (WSVR). Finally, WSVR is utilized as the base learner for Adaptive Boosting (AdaBoost), yielding the XGBoost–WSVR–AdaBoost model based on feature weighting. The proposed model outperforms AdaBoost, RNN, ERT, LSTM, and other models in terms of Mean Absolute Percentage Error (MAPE) and goodness-of-fit ( $${R}^{2}$$ R 2 ). It also exhibits superior predictive performance compared to models in ablation experiments, and the introduction of text data or Google trends further improves the prediction performance of the model. In conclusion, the improved AdaBoost feature-weighted combination model proposed in this study offers a more accurate prediction for semiconductor price index.