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

Jun Hao · Jianping Li · Xiaolei Sun

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Forecasting Techniques and Applications Stock Market Forecasting Methods Time Series Analysis and Forecasting

Abstract

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.

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References

2002Comparing Predictive Accuracy 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm 2017A deep learning ensemble approach for crude oil price forecasting 2016Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation 2004A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates 2016Exchange Rates and Monetary Policy Uncertainty 2020Retail sales forecasting with meta-learning 2020A novel text-based framework for forecasting agricultural futures using massive online news headlines

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