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

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...