Forecasting the Highest and Lowest Prices in Financial Markets Using a VMD-LSTM Hybrid Model

I Made Adi Purwantara; Kusrini Kusrini; Arief Setyanto; Ema Utami

doi:10.31154/cogito.v11i2.963.295-310

Authors

I Made Adi Purwantara Universitas Amikom Yogyakarta
Kusrini Kusrini Universitas Amikom Yogyakarta
Arief Setyanto Universitas Amikom Yogyakarta
Ema Utami Universitas Amikom Yogyakarta

DOI:

https://doi.org/10.31154/cogito.v11i2.963.295-310

Keywords:

VMD, LSTM, Decomposition, Deep Learning, Financial Market

Abstract

Accurate forecasting of the lowest and highest prices in financial markets poses a considerable challenge due to the inherent nonlinear behaviour, non-stationarity, and high noise levels of financial time series data. Most prior studies focus only on closing prices, with limited attention to the simultaneous prediction of high and low prices. Yet, predicting the lowest and highest prices is essential for investors to make informed trading decisions. To address this gap, this study proposes a hybrid DL framework that integrates VMD and LSTM networks for predicting daily high and low prices simultaneously. This study used 12 years of daily data from three diverse assets: AUD/USD, TLKM, and XAU/USD. The data underwent preprocessing, VMD-based decomposition, and were input into the LSTM. The dataset was split 80% for training and 20% for testing. Experiments varied the number of decomposition modes (K = 7, 10, 12) and sliding window sizes (5, 15, 30, 45, 60, 90). Results show that the VMD-LSTM model exhibits improved performance in most of the tested scenarios compared to traditional LSTM. These findings underscore that the use of VMD decomposition can help enhance the accuracy of forecasting the highest and lowest prices in the financial market.

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