Supervisor
Matt Lemon
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
Forecasting volatile time series, such as commodity prices and market volatility indices, is critical for financial and operational decision-making. This study evaluates the impact of Variational Mode Decomposition (VMD) on hybrid forecasting models combining ARIMA, GARCH, and LSTM. Using gold, platinum, and VIX datasets, models were assessed via MAE, RMSE, and computational time. Results show that VMD improves ARIMA forecasts, and achieves optimal performance when combined with ARIMA-LSTM using performance weighting reducing MAE by 0.62-3.81 and reducing RMSE by 1.48-6.20. Conversely, VMD integration with ARIMA-GARCH and LSTM decreased accuracy. While VMD enhances predictive performance, it increases computational time substantially, increasing from 12mins for ARIMA-LSTM to 1hr 15mins for VMD-ARIM-LSTM. This highlights a trade-off between accuracy and efficiency. The findings suggest that VMD is most effective in hybrid ARIMA-LSTM architectures for volatile data, providing a practical framework for industries requiring precise forecasts.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Allen, D.
(2025) Evaluating the Effectiveness of Variational Mode Decomposition(VMD)-Enhanced Hybrid Models for Forecasting Volatile Price and Indicator Time Series. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.98