SUDF-RS: A new foreign exchange rate prediction method considering the complementarity of supervised and unsupervised deep representation features

Abstract

The supervised and unsupervised deep representation features have been adopted and verified their effective roles separately in the foreign exchange rate prediction (FERP). However, the complementarity of these two types of features for FERP remained unexplored. To address this problem, we proposed a novel method for one day ahead FERP, which improves Random Subspace by simultaneously considering both Supervised and Unsupervised Deep representation Features, namely, SUDF-RS. Feature extraction and model construction are two important stages in the SUDF-RS method. Firstly, the supervised and unsupervised deep representation features are extracted by the long short-term memory networks and deep belief networks respectively. Secondly, an improved RS method, which incorporates random forest-based feature weighting mechanism is developed to generate high-quality feature subsets. Thirdly, each feature subset is used to train the corresponding base learner and the final results are obtained by averaging the results of each base learner. Experiments on three exchange rate datasets, namely EUR/USD, GBP/USD and USD/JPY validate that the proposed SUDF-RS significantly outperforms the benchmark methods in terms of both MAPE and RMSE.