Increasing the prediction performance of temporal convolution network using multimodal combination input: Evidence from the study on exchange rates

Abstract

The currency market is one of the most important financial markets in the world. The exchange rate movement has effect on international trade and capital flow. This study presents a forecasting method for exchange rate based on multi-modal combination market trend. The method facilitates the more accurate identification of volatility link between exchange rates, unlike the conventional ones, in which only information related to itself is used as input. We select multiple characteristics of the exchange rate from other countries as input data. Then the Pearson correlation coefficient and random forest model are used to filter these characteristics We integrate the data with higher correlation into the temporal convolutional network model to forecast the exchange rate. For the empirical samples, a nine-year period historical exchange rates of the Euro, Ruble, Australian dollar, and British pound corresponding to the Renminbi are used. The empirical results show the more stable effect using the forecasting method proposed in this study than the traditional models.