| Summary: | 碩士 === 國立暨南國際大學 === 資訊管理學系 === 106 === The automobile industry plays an important role when it comes to economic development of a country. America’s automobile industry has been a bright spot in the global economy. It is related to a wide range of neighboring industries, such as steel, transportation, and component supply. The increase of capacity utilization can create more job opportunities that will have a far-reaching impact on economic structure. For years, social media websites have been spreading widely, and make it easier for people to share their comments about products and services of brands. People are now able to seek information and exchange their opinions in social media, that eventually may effect one’s purchase intention and behavior. For these reasons, many researchers and practitioners increasingly resort to social media to obtain valuable information and potential business opportunities.
In this study, we focus on monthly new vehicle sales in the US. The dataset consists of three kinds of input variables. One is sentiment data set, another is stock market index data set and the other is the combination of the first two data sets. The sentiment data set use tweets data to get the public opinion about buying cars by sentiment analysis. Dow Jones Industrial Average (DJI) and Standard & Poor's 500 (S&P 500) are selected as the performance of purchasing power and market economy. The aim of this study is to predict light vehicle and total vehicle sales in the US, and the Least Squares Support Vector Regression (LSSVR) method is used to construct a vehicle sales forecasting model. For forecasting accuracy comparison, the mean absolute percentage error (MAPE) and root mean square error (RMSE) are computed. Then, compare with five time series models: Naïve、Exponential smoothing、Holt-Winters、ARIMA and SARIMA. Furthermore, this paper explores the usefulness of raw data and seasonally adjusted data for vehicle sales forecasting.
Our empirical analysis indicates that using the combination of sentiment and stock market index data as the input variables of LSSVR model has the best predictive results. Comparisons of prediction accuracy demonstrate that our model outperforms other time series models. Moreover, LSSVR models with seasonally adjusted data perform better than unadjusted raw data, and the prediction accuracy of the proposed method is improved by approximately 35%.
|
|---|
