A Novel Hybrid Model for Gasoline Prices Forecasting Based on Lasso and CNN

Abstract

Gasoline is the lifeblood of the national economy. The forecasting of gasoline prices is difficult because of frequent price fluctuations, its complex nature, diverse influencing factors, and low accuracy of prediction results. Previous studies mainly focus on forecasting gasoline prices in a single region by single time series analysis which ignores the daily price co-movement of different series from multiple regions. Because price co-movement may contain useful information for price forecasting, this paper proposes the Lasso-CNN ensemble model that combines statistical models and deep neural networks to forecast gasoline prices. In this model, the Least Absolute Shrinkage and Selection Operator (Lasso) screens and chooses the correlated time series to enhance the performance of forecasting and avoid overfitting, while Convolutional Neural Network (CNN) takes the selected multiple series as its input and then forecasts the gasoline prices in a certain region. Forecasting results of gasoline prices at the national level and regional levels by using the new method demonstrate that the new approach provides more accurate results for the predictions of gasoline prices than those results generated by alternative methods. Thus, the relevant series can enhance the performance of forecasting and help to gain better results.