Performance Evaluation of Univariate Time Series and Deep Learning Models for Foreign Exchange Market Forecasting: Integration with Uncertainty Modeling
Abstract
Foreign exchange market is the largest financial market in the world and thus prediction of foreign exchange rate values is of interest to millions of people. In this research, I evaluated the performance of Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Autoregressive Integrated Moving Average (ARIMA) and Moving Average (MA) on the USD/CAD and USD/AUD exchange pairs for 1-day, 1-week and 2-weeks predictions. For LSTM and GRU, twelve macroeconomic indicators along with past exchange rate values were used as features using data from January 2001 to December 2019. Predictions from each model were then integrated with uncertainty modeling to find out the chance of a model’s prediction being greater than or less than a user-defined target value using the error distribution from the test dataset, Monte-Carlo simulation trials and ChancCalc excel add-in. Results showed that ARIMA performs slightly better than LSTM and GRU for 1-day predictions for both USD/CAD and USD/AUD exchange pairs. However, when the period is increased to 1-week and 2-weeks, LSTM and GRU outperform both ARIMA and moving average for both USD/CAD and USD/AUD exchange pair.
Degree
M.Sc.
Advisors
Elkin, Purdue University.
Subject Area
Artificial intelligence|Economics|Finance
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