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

Abstract

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.

Trend- and Periodicity-Trait-Driven Gasoline Demand Forecasting

“…This shows that medium-or long-term predictions are more difficult than short-term ones, and thus in-depth research on medium-and long-term forecasting models is needed in the future. However, the decomposition-ensemble models show better prediction results than the single models, which also confirms the superiority of the decomposition-ensemble forecasting model [16].…”

“…In order to test the effectiveness of the predictive model, this article uses three commonly used predictive indicators, mean absolute percent error (MAPE), root mean squared error (RMSE), and directional statistic (D stat ) to evaluate the prediction accuracy [16]. In particular, MAPE and RMSE are used to measure the of level accuracy, and D stat is used to evaluate the accuracy of the direction predictions.…”

“…Yu et al [15] adopted an effective rolling decomposition-ensemble model for gasoline consumption forecasting and obtained a better prediction performance than that of the benchmark models listed in the study. Similarly, Yu and Ma [16] proposed a data-trait-driven rolling decomposition-ensemble model for gasoline consumption forecasting and obtained good prediction performance.…”