A weighted ensemble model combining ARIMA, LSTM, and GBM for robust time series prediction
https://doi.org/10.17586/2226-1494-2025-25-6-1150-1159
Abstract
Time series forecasting has been used in research and applications in a number of domains such as environmental forecasting, healthcare, finance, supply chain management, and energy consumption. Accurate prediction of future values is necessary for strategic planning operational efficiency and well-informed decision-making regarding time-dependent variables. A hybrid time series forecasting architecture is proposed that combines the strengths of machine learning and statistical models, in particular Gradient Boosting Machines (GBM), Auto-Regressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM) networks. While LSTM networks and GBM are able to capture complex dependencies and nonlinear patterns, the ARIMA model captures the linear components within the time series. The hybrid model exploits ARIMA interpretability, LSTM temporal memory ability, and GBM ensemble learning efficiency by integrating these three models. Comprehensive experiments conducted on benchmark data sets have shown that the accuracy and reliability of predictions of the proposed hybridization significantly exceeds both individual models and traditional baseline models. The results show that for a variety of real-world applications, hybrid architectures can deliver reliable and accurate time series predictions.
About the Authors
A. Vignesh
SRM Institute of Science and Technology — Ramapuram
India
India
Arumugam Vignesh, Scientific Researcher
600087; Chennai
sc 57219977662
N. Vijayalakshmi
SRM Institute of Science and Technology — Ramapuram
India
India
Natarajan Vijayalakshmi, PhD, Associate Professor
600087; Chennai
sc 57212868093
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Review
For citations:
Vignesh A., Vijayalakshmi N. A weighted ensemble model combining ARIMA, LSTM, and GBM for robust time series prediction. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2025;25(6):1150-1159. https://doi.org/10.17586/2226-1494-2025-25-6-1150-1159
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