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A Research of Fine-Tuning CNN-LSTM Model for Gold Price Prediction
- 1 School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai, 519070, China
* Author to whom correspondence should be addressed.
Abstract
Gold price and foreign exchange price prediction are usually regarded as serialized data with strong time correlation. It is precisely because of this feature that the performance of traditional convolutional neural networks is often not as good as that of recurrent neural networks. This paper fine-tunes the CNN-LSTM model in two ways. The first is to simplify the input data, convert the time series into binary data, and change the model task from predicting prices to predicting price fluctuations. The second is to add a dropout layer and use the dropout layer to improve the model's overfitting problem. The results demonstrate that the improved model outperforms the first model, with prediction accuracy rising from 54% to 57%. Furthermore, the refined model is not limited to processing the gold price series; it can also be applied to other time series, and when paired with other models, it could yield even better outcomes.
Keywords
CNN-LSTM Model, Gold Price Prediction, Time Series Data
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Cite this article
Luo,Y. (2024). A Research of Fine-Tuning CNN-LSTM Model for Gold Price Prediction. Advances in Economics, Management and Political Sciences,94,157-163.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of ICFTBA 2024 Workshop: Finance in the Age of Environmental Risks and Sustainability
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