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.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/17169245 |
| Date | 13 December 2021 |
| Creators | Wajahat Waheed (11828201) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/PERFORMANCE_EVALUATION_OF_UNIVARIATE_TIME_SERIES_AND_DEEP_LEARNING_MODELS_FOR_FOREIGN_EXCHANGE_MARKET_FORECASTING_INTEGRATION_WITH_UNCERTAINTY_MODELING/17169245 |
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