Name
Title | ||
|---|---|---|
Precious Metal Price Prediction Based On Deep Regularization Self-Attention Regression |
Abstract | ||
|---|---|---|
It is non-trivial to predict the prices of precious metals since a number of factors can affect the fluctuations of precious metal prices. Either parametric models or machine learning models cannot accurately forecast the precious metal prices. Though deep learning approaches show their strengths in extracting key features from complicated data, they have the limitations of learning localization and losing some temporal and spatial features. The recent advances in attention mechanisms bring the opportunities to overcome the limitation of deep learning models. In this paper, we originally propose a Regularization Self-Attention Regression Model for precious metal price prediction. In particular, the proposed RSAR model consists of convolutional neural network (CNN) component and Long Short-Term Memory Neural Networks (LSTM) component. Integrating with self-attention mechanism, this model can extract both spatial and temporal features from precious metal price data. Meanwhile, the proper configuration of regularization functions can also lead to the further performance improvement. Extensive experiments on realistic precious metal price dataset show that our proposed approach outperforms other conventional machine learning and deep learning methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2019.2962202 | IEEE ACCESS |
Keywords | Field | DocType |
Long short-term memory, convolutional neural network, attention mechanism, flnancial data analysis, deep learning | Parametric model,Convolutional neural network,Computer science,Regression analysis,Regularization (mathematics),Precious metal,Artificial intelligence,Deep learning,Artificial neural network,Machine learning,Distributed computing,Performance improvement | Journal |
Volume | ISSN | Citations |
8 | 2169-3536 | 2 |
PageRank | References | Authors |
0.38 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junhao Zhou | 1 | 6 | 2.80 |
| Zhanhong He | 2 | 2 | 0.38 |
| Ya Nan Song | 3 | 2 | 0.38 |
| Hao Wang | 4 | 216 | 56.92 |
| Xiaoping Yang | 5 | 2 | 0.38 |
| Wenjuan Lian | 6 | 2 | 0.38 |
| Hongning Dai | 7 | 629 | 62.25 |