A bi‐level ensemble learning approach to complex time series forecasting: Taking exchange rates as an example

Abstract

Forecasting complex time series faces a huge challenge due to its high volatility. To improve the accuracy and robustness of prediction, this paper proposes a bi‐level ensemble learning approach by combining decomposition‐ensemble forecasting and resample strategies. The bi‐level ensemble approach consists of four steps: data decomposition via singular spectrum analysis (SSA), resampling by employing a bagging algorithm, individual forecasting utilizing extreme learning machine (ELM), and introducing sorting‐pruning and simple addition ensemble strategies for integrating the inner and outer results, respectively. To verify the effectiveness of the established forecasting approach, three exchange rate time series are selected as samples. The results reveal that the proposed model is significantly better than the other benchmarks at different lead times, which indicates that it can be regarded as an effective and promising tool for complex time series forecasting.

Do multisource data matter for NGP prediction? Evidence from the G-LSTM model

“…In this paper, the root mean square error (RMSE), mean square error (MSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) were selected to evaluate the accuracy of the model [ 54 ], as shown in Equations (32) , (33) , (34) , (35) . In addition, Diebold–Mariano (DM) statistical testing was introduced to determine whether the prediction accuracy of Model A is significantly better than that of Model B.…”