Market Forecasting Using LSTM-ARIMA Model with MACD Decomposition
Journal
2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2025
Start Page
1458
End Page
1463
ISBN (of the container)
979-833157206-8
Date Issued
2025-11-28
Author(s)
Yu, Teng-Chih
Abstract
Market forecasting plays a critical role in economic signal processing. In this study, a model that applies the moving average convergence divergence (MACD) indicator together with learning-based architectures is proposed for market forecasting. First, we propose a novel MACD decomposition method to preprocess the data. By employing the proposed decomposition approach, the high- and low-frequency components are effectively separated. These components correspond to the short-term and the long-term information in the market, respectively. Then, two well-known models in economic forecasting, LSTM and ARIMA models, are employed to perform next-day predictions using only past closing prices. The proposed algorithm well integrates the merits of learning-based techniques and classical regression model. Despite of relatively less complexity, the proposed approach achieves promising performance and demonstrates adaptability across a variety of economic signals.
Event(s)
17th Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2025
Publisher
IEEE
Type
conference paper