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

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...

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Vydáno v:	Neural computing & applications Ročník 32; číslo 23; s. 17351 - 17360
Hlavní autoři:	Livieris, Ioannis E., Pintelas, Emmanuel, Pintelas, Panagiotis
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.12.2020 Springer Nature B.V
Témata:	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
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0941-0643 1433-3058
DOI:	10.1007/s00521-020-04867-x