A CNN–LSTM model for gold price time-series forecasting

• Published in: Neural computing & applications Vol. 32; no. 23; pp. 17351 - 17360
• Main Authors: Livieris, Ioannis E., Pintelas, Emmanuel, Pintelas, Panagiotis
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2020; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Deep learning; Forecasting; Gold; Image Processing and Computer Vision; Machine learning; Mathematical models; Prediction models; Probability and Statistics in Computer Science; S.I. : Emerging applications of Deep Learning and Spiking ANN; Deep learning; Gold price forecasting; Convolutional neural networks; Time series
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-020-04867-x

Gold price volatilities have a significant impact on many financial activities of the world. The development of a reliable prediction model could offer insights in gold price fluctuations, behavior and dynamics and ultimately could provide the opportunity of gaining significant profits. In this work, we propose a new deep learning forecasting model for the accurate prediction of gold price and movement. The proposed model exploits the ability of convolutional layers for extracting useful knowledge and learning the internal representation of time-series data as well as the effectiveness of long short-term memory (LSTM) layers for identifying short-term and long-term dependencies. We conducted a series of experiments and evaluated the proposed model against state-of-the-art deep learning and machine learning models. The preliminary experimental analysis illustrated that the utilization of LSTM layers along with additional convolutional layers could provide a significant boost in increasing the forecasting performance.