PHM-MAX-XGBoost-LSTM-SVM: A Hybrid Model for Time Series Forecasting – A Comparative Analysis with Traditional and Modern Approaches

Abstract

This paper presents a comparative analysis of the PHM-MAX-XGBoost-LSTM-SVM hybrid model for time series forecasting, evaluated against traditional statistical, machine learning, and deep learning methods. It addresses key challenges in time series data, including noise, missing values, and non-stationarity, and explores preprocessing techniques such as data cleaning, transformation, and dimensionality reduction, with mathematical foundations and domain-specific relevance. The study benchmarks the hybrid model on various time series types—univariate, multivariate, seasonal, and irregular—across sectors like finance, energy, and healthcare. Strengths and limitations of each approach are discussed, with an emphasis on model performance, adaptability, and interpretability. Results highlight the potential of hybrid architectures to outperform standalone models. Future research directions include enhancing preprocessing integration, handling sparse data, and advancing real-time forecasting capabilities