Forecasting exchange rates with ensemble neural networks and ensemble K-PLS: A case study for the US Dollar per Indian Rupee

The purpose of this paper is to evaluate and benchmark ensemble methods for time series prediction for daily currency exchange rates using ensemble feedforward neural networks and kernel partial least squares (K-PLS). Best-practice forecasting methods for the US Dollar (USD) per Indian Rupee (IR) are applied for training, validating, and testing the machine learning models. In order to perform the benchmarking evaluation study neural network forecasting methods are first compared on a benchmarked neural network time series prediction method for the Canadian Lynx time series. The K-PLS method is benchmarked in addition with support vector machines (SVM), a similar kernel-based method. Both one-step ahead and a roll-out methods for extended forecast horizons are applied for the currency exchange rates. The paper is novel in the sense that two new ensemble methods are introduced: weight seeding and multiple cross-validation averaging. The paper is also novel in the sense that several new validation indices are proposed that are especially applicable for time series: q² and Q² and the fraction of misses in the exchange rate return space, which is a more relevant metric for currency speculation. As a general conclusion it is found that the USD per IR is quite predictable, while other currencies such as the USD per Euro and the Australian Dollar (AUD) per Euro are not predictable.