Time-Series Forecasting Models for Gasoline Prices in China
| Autor: | Feng Xu, Julius N. Anyu, Jian Hua, Mohamad Sepehri, Sergey Ivanov |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Inflation
Artificial neural network Computer science 020209 energy media_common.quotation_subject 05 social sciences Exponential smoothing Time horizon 02 engineering and technology Data-driven Support vector machine 0502 economics and business 0202 electrical engineering electronic engineering information engineering Econometrics Autoregressive integrated moving average 050207 economics Time series media_common |
| Zdroj: | International Journal of Economics and Finance. 10:43 |
| ISSN: | 1916-9728 1916-971X |
| DOI: | 10.5539/ijef.v10n12p43 |
| Popis: | Accurate prediction of gasoline price is important for the automobile makers to adjust designs and productions as well as marketing plans of their products. It is also necessary for government agencies to set effective inflation monitoring and environmental protection policies. To predict future levels of the gasoline price, due to difficulties of obtaining accurate estimates of influential external factors, data driven time-series forecasting models thus become more suitable given the convenience and practicability they are providing. In this paper, five popular time-series forecasting models, i.e., ARIMA-GARCH, exponential smoothing, grey system, neural network, and support vector machines models, are applied to predict gasoline prices in China. Comparing the performances of these models, it is noted that for this specific time series, a parsimonious ARIMA model performs the best in predicting the gasoline prices for a short time horizon, while for the medium length and long run the SVR and FNN models outperforms others respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|