Abstract
The fluctuations of economic and financial time series are influenced by various kinds of factors and usually demonstrate strong nonstationary and high complexity. Therefore, accurately forecasting economic and financial time series is always a challenging research topic. In this study, a novel multidecomposition and self-optimizing hybrid approach integrating multiple improved complete ensemble empirical mode decompositions with adaptive noise (ICEEMDANs), whale optimization algorithm (WOA), and random vector functional link (RVFL) neural networks, namely, MICEEMDAN-WOA-RVFL, is developed to predict economic and financial time series. First, we employ ICEEMDAN with random parameters to separate the original time series into a group of comparatively simple subseries multiple times. Second, we construct RVFL networks to individually forecast each subseries. Considering the complex parameter settings of RVFL networks, we utilize WOA to search the optimal parameters for RVFL networks simultaneously. Then, we aggregate the prediction results of individual decomposed subseries as the prediction results of each decomposition, respectively, and finally integrate these prediction results of all the decompositions as the final ensemble prediction results. The proposed MICEEMDAN-WOA-RVFL remarkably outperforms the compared single and ensemble benchmark models in terms of forecasting accuracy and stability, as demonstrated by the experiments conducted using various economic and financial time series, including West Texas Intermediate (WTI) crude oil prices, US dollar/Euro foreign exchange rate (USD/EUR), US industrial production (IP), and Shanghai stock exchange composite index (SSEC).
Highlights
Economic and financial time series, such as price movements, stock market indices, and exchange rate, are usually characterized by strong nonlinearity and high complexity, since they are influenced by a number of extrinsic and intrinsic factors including economic conditions, political events, and even sudden crises [1, 2]
To better forecast economic and financial time series, we propose a novel multidecomposition and self-optimizing ensemble prediction model MICEEMDAN-whale optimization algorithm (WOA)-random vector functional link (RVFL) combining multiple ICEEMDANs, WOA, and RVFL networks
WOA is introduced to optimize RVFL networks to further improve the prediction accuracy. irdly, the predictions of subseries in each decomposition are integrated into the forecasting results of each decomposition using addition
Summary
Economic and financial time series, such as price movements, stock market indices, and exchange rate, are usually characterized by strong nonlinearity and high complexity, since they are influenced by a number of extrinsic and intrinsic factors including economic conditions, political events, and even sudden crises [1, 2]. E frequently used statistical approaches for economic and financial time series forecasting include the error correction model (ECM) [3], hidden Markov model (HMM) [4], random walk (RW) model [5], autoregressive moving average (ARMA) model [6], autoregressive integrated moving average (ARIMA) model [7], and generalized autoregressive conditional heteroskedasticity (GARCH) model [8, 9]. Hassan and Nath developed the HMM approach for forecasting stock price for interrelated markets [4]. Rout et al integrated ARMA with differential evolution (DE) to develop a hybrid model for exchange rate forecasting [6]. Alberg et al conducted a comprehensive analysis of the stock indices using various GARCH models, and the experimental results showed that the asymmetric GARCH model enhanced the overall prediction performance [9]
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