Ensemble Empirical Mode Decomposition Model Research Articles

A three-stage framework for vertical carbon price interval forecast based on decomposition–integration method

In the current context of pursuing carbon neutrality and carbon peaking, many countries are accelerating the construction of carbon trading markets. Accurate prediction of carbon prices can enable national carbon trading markets to play a role in carbon emission reduction as soon as possible. However, current research is limited mostly to point forecasting of carbon prices, which makes it difficult to guarantee the stability of forecasting results in an increasingly complex market. Therefore, this paper proposes a three-stage vertical carbon price interval prediction framework. The contributions of this paper are as follows: the selection process of the decomposition model is regarded as an important process of prediction; a backpropagation neural network optimized by the sparrow search algorithm (SSA-BPNN) is used for the point prediction of carbon prices as a first attempt; and the kernel density estimation (KDE) model is used for interval estimation based on the point prediction results, which improves the confidence of the prediction. To validate the framework, this paper uses Shenzhen SZA-2014 products as the sample. The results show that the root mean square error of the predicted result with the improved complete ensemble empirical mode decomposition model (ICEEMD) is reduced by 29.7% and the use of SSA increases the predicted R2 by 8.5% compared with other optimization algorithms. In addition, the prediction interval coverage probability of interval prediction reaches 86% under 70% confidence. These results show that the proposed framework is not only more effective in point prediction but also performs well in interval prediction.

The impact of oil price on the clean energy metal prices: A multi-scale perspective

To examine the spillover effects of crude oil price and clean energy metal prices, we first explore the Granger causality between crude oil and clean energy metals prices. Then, we decompose crude oil price returns into a high frequency sequence (Oil_HF), a low frequency sequence (Oil_LF), and a trend residual (Oil_res) using the ensemble empirical mode decomposition model. Lastly, a VAR model is used to study the spillover effects of crude oil price on seven types of clean energy metal prices. The results show that crude oil price has non-linear Granger causality with lithium, cobalt, manganese, antimony, cadmium, molybdenum, and tellurium. And crude oil price has a significant positive spillover effect on seven types of clean energy metals at different time scales. Specifically, the crude oil original sequence, Oil_HF, Oil_LF, and Oil_res all have fluctuation effects on the cobalt and molybdenum markets. The spillover effects of the crude oil market on the lithium, manganese, antimony, and cadmium markets are caused by the low frequency sequence. The trend in crude oil price returns has a great effect on tellurium price, while the spillover effects of crude oil price, Oil_HF, and Oil_LF on tellurium are weak positive.

Can economic policy uncertainty, oil prices, and investor sentiment predict Islamic stock returns? A multi-scale perspective

This study investigates the causal relationships between economic policy uncertainty, oil prices, investor sentiment, and stock returns of nine Dow Jones Islamic Market Indices. Specifically, this study analyzes the causal effect of these variables on Islamic stock returns at different time scales using the ensemble empirical mode decomposition model. First, we decompose the economic policy uncertainty proxy, oil prices, and investor sentiment into different independent components called intrinsic mode functions (IMFs): short-term IMFs designate the effects of irregular events; medium-term IMFs present the effects of extreme events; and long-term IMFs capture long-term effects. Second, we employ a nonlinear non-parametric causality model to test the causal relationship between different variables and Islamic stock returns at both the original and decomposed levels. We find causal relationships between the underlying variables and Islamic stock returns in several time frequencies rather than in the whole sample period. Our results suggest that the use of lagged economic policy uncertainty, oil prices, and investor sentiment may improve the predictability of Islamic stock returns. A test of forecast accuracy indicated the robustness of our results.

Fast ensemble empirical mode decomposition model for forecasting crude oil and condensates

Crude oil and condensates supply and demand strives to be main authority of the sustenance of almost all country’s economy. The sudden rise in the oil price has forced the government to forecast the supply and demand of crude oil and condensates in order to make sure that the amount of crude oil meets the supply and demand of the country. Accurate forecasts can save cost, foresee scarcity of demand, and help in budgeting profit. In addition, predicting crude oil and condensate data is frequently proven to be a demanding task considering the various intricacies of oil data pattern. The main objective of this study was to forecast crude oil and condensates demand data in Malaysia using Fast Ensemble Empirical Mode Decomposition (FEEMD) model. The forecasting process using FEEMD model was performed in order to achieve the most desirable forecast accuracy of the crude oil and condensates data. The FEEMD model is an extension of the Empirical Mode Decomposition (EMD) model whereby white noise signal was added to the existing signal in the sifting process. The effectiveness of the proposed forecasting method was compared to other traditional models of ARIMA, ARIMAX and GARCH. The results revealed that the proposed FEEMD method for forecasting crude oil and condensates data was very promising as it achieved good forecast accuracy.

Identifying the dynamic relationship between tanker freight rates and oil prices: In the perspective of multiscale relevance

The tanker shipping market has been treated as the key extension of the world oil market and inevitably, its uncertainty is correlated to volatility of the oil market, besides supply and demand factors. Therefore, for improving operational management and budget planning decisions, it is essential to understand the inherent relevance between freight rates and crude oil prices. Taking time-dependent features into account, this paper focuses on the multiscale correlation between freight rates and oil prices. Given the complexity and mutability of tanker freight rate process, this paper first extracts the intrinsic mode functions from the original data using the Ensemble Empirical Mode Decomposition model and then reconstructs two separate composite functions: high-frequency and low-frequency components, plus the residual as the long-term trend. Secondly, correlations of the multiscale components of freight rates and oil prices are examined based on relevance structure. Empirical results show that tanker freight rates and oil prices exhibit different multiscale properties with true economic meaning and are significantly correlated in the medium and the long term when taking the relevance structure into account. These findings offer some useful information to better understand the correlations between these two markets and more importantly, propose a novel perspective to investigate the dynamic relationship between two markets.