An effective rolling decomposition-ensemble model for gasoline consumption forecasting

Yu, Lean; Ma, Yueming; Ma, Mengyao

doi:10.1016/j.energy.2021.119869

Cited by 45 publications

(9 citation statements)

References 23 publications

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“…This implies that the introduction of the rolling decomposition and prediction mechanism is very effective in improving the prediction performance. This result is consistent with the previous finding in Yu et al [65].…”

Section: Data Decomposition Analysissupporting

confidence: 94%

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A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting

2021

Energies

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In order to predict the gasoline consumption in China, this paper propose a novel data-trait-driven rolling decomposition-ensemble model. This model consists of five steps: the data trait test, data decomposition, component trait analysis, component prediction and ensemble output. In the data trait test and component trait analysis, the original time series and each decomposed component are thoroughly analyzed to explore hidden data traits. According to these results, decomposition models and prediction models are selected to complete the original time series data decomposition and decomposed component prediction. In the ensemble output, the ensemble method corresponding to the decomposition method is used for final aggregation. In particular, this methodology introduces the rolling mechanism to solve the misuse of future information problem. In order to verify the effectiveness of the model, the quarterly gasoline consumption data from four provinces in China are used. The experimental results show that the proposed model is significantly better than the single prediction models and decomposition-ensemble models without the rolling mechanism. It can be seen that the decomposition-ensemble model with data-trait-driven modeling ideas and rolling decomposition and prediction mechanism possesses the superiority and robustness in terms of the evaluation criteria of horizontal and directional prediction.

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Section: Data Decomposition Analysissupporting

confidence: 94%

“…This method can make full use of existing data to ensure the maximum knowledge learning. More details about rolling decomposition and prediction mechanism can refer to the Yu et al [65].…”

Section: Rolling Decomposition and Forecasting Mechanismmentioning

confidence: 99%

A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting

2021

Energies

Self Cite

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“…Preprocessing of raw data is one of the difference between the three types of models above and the last type of model. The decomposition and ensemble models have been widely applied in many different fields as seen from previous literature, like gasoline consumption forecasting ( 34 ), marine cargo volume ( 35 ), foreign exchange rates forecasting ( 36 ), and container throughput forecasting ( 37 – 40 ). For example, to better predict container throughput, Du et al ( 37 ) introduced a decomposition and ensemble model named variational mode decomposition–butterfly extreme learning machine–error correction strategy (VMD-BELM-ECS); its mean absolute error (MAE) is 2.8465, whereas ARIMA’s MAE is 13.4 and least-square support vector machine’s MAE is 12.64.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

Xiao

Xue

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Container shipping has suffered a sharp decline since COVID-19, and risks associated with container transit will persist in the future. The decrease in container transportation has caused a ripple impact on the global supply chain. However, container throughput forecasting is both critical and complicated under the circumstances of economic uncertainty and the outbreak of the COVID-19 pandemic. A novel model propounded in this paper for container throughput forecasting to assist the port management bureau and container shipping industry integrates with the variational mode decomposition (VMD) algorithm, SARIMA technique, convolutional neural network (CNN) method, long short-term memory (LSTM) approach, and attention mechanism, among others. In this model, there are three stages: (i) data decomposition, (ii) component prediction, and (iii) ensemble output. In the first stage, the original data of the container throughput time series is decomposed into several different components using the VMD algorithm. Next, from low frequency to high frequency, each component is modeled by the corresponding prediction approach. Subsequently, the prediction results of each component generated by the previous stage are integrated into the final forecasting results by addition strategy. To enhance the prediction accuracy in the second stage, the attention mechanism is adopted in the CNN-bidirectional LSTM method. Finally, six measurement criteria, the container throughput times series at four ports, and a statistical evaluation approach are applied to comprehensively evaluate the proposed model compared with seven benchmark models. The empirical analysis demonstrates that the proposed model significantly outperforms other comparable models with regard to prediction results, level, and directional prediction accuracy.

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“…In Ref. [32], they presented an excellent rolling decompositionensemble model for gasoline forecasting, which was both accurate and efficient. e researchers' experimental results demonstrate that the rolling decomposition-ensemble model is both accurate and resilient when it comes to projecting gasoline consumption levels and trends.…”

Section: Related Workmentioning

confidence: 99%

An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning

et al. 2022

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This paper aims to introduce a robust framework for forecasting demand, including data preprocessing, data transformation and standardization, feature selection, cross-validation, and regression ensemble framework. Bagging (random forest regression (RFR)), boosting (gradient boosting regression (GBR) and extreme gradient boosting regression (XGBR)), and stacking (STACK) are employed as ensemble models. Different machine learning (ML) approaches, including support vector regression (SVR), extreme learning machine (ELM), and multilayer perceptron neural network (MLP), are adopted as reference models. In order to maximize the determination coefficient ( R 2 ) value and reduce the root mean square error (RMSE), hyperparameters are set using the grid search method. Using a steel industry dataset, all tests are carried out under identical experimental conditions. In this context, STACK1 (ELM + GBR + XGBR-SVR) and STACK2 (ELM + GBR + XGBR-LASSO) models provided better performance than other models. The highest accuracies of R2 of 0.97 and 0.97 are obtained using STACK1 and STACK2, respectively. Moreover, the rank according to performances is STACK1, STACK2, XGBR, GBR, RFR, MLP, ELM, and SVR. As it improves the performance of models and reduces the risk of decision-making, the ensemble method can be used to forecast the demand in a steel industry one month ahead.

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An effective rolling decomposition-ensemble model for gasoline consumption forecasting

Cited by 45 publications

References 23 publications

A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting

A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting

Novel Decomposition and Ensemble Model with Attention Mechanism for Container Throughput Forecasting at Four Ports in Asia

An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning

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