An Application of AdaBoost-GRU Ensemble Model to Economic Time Series Prediction

Objectives: Given the importance of accurate prediction of financial time series data and their benefits in the real-life, AdaBoost-GRU ensemble learning is proposed in which it’s forecasting accuracy is to be compared with AdaBoost-LSTM, single Long Short Term Memory (LSTM), and single Gated Recurrent Unit (GRU). Methods: The data for Korea Composite Stock Price Index (KOSPI) obtained from Naver Finance from January 2000 to April 2020, the Oil Price data for the entire Gyeongnam region among domestic oil price data obtained from Korea Petroleum Corporation (Opinet) and USD Exchange data provided by Naver Financial from April 2004 to May 2020 were employed. The analyses were made using mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE) as the performance metric. Findings: Empirical results show that the proposed method outperforms all other models that serve as benchmarked models, in all three kinds of data used in this research. This also shows that ensemble models have better performance than the single models as both AdaBoost-GRU and AdaBoost-LSTM outperform their respective single GRU and single LSTM. Novelty/Applications: This empirical study suggests that the AdaBoost-GRU ensemble-learning model is a highly promising approach for forecasting these kinds of data. However, another ensemble model that can combine AdaBoost with other single models such as ConvD1 can be developed and applied. Keywords: Oil Price; Exchange Rate; Stock Price Index; Time Series Forecasting; AdaBoost Algorithm; Gated Recurrent Unit