LAUSR

Owning to the booming of social media, making comments or expressing opinions about merchandises online becomes easier than before. Data from social media might be one of the essential inputs for forecasting sales of vehicles. Besides, some other effects, such as stock market values, have influences on purchasing power of vehicles. In this paper, both multivariate regression models with social media data and stock market values and time series models are employed to predict monthly total vehicle sales. The least squares support vector regression (LSSVR) models are used to deal with multivariate regression data. Three types of data, namely sentiment scores of tweets, stock market values, and hybrid data, are employed in this paper to forecast monthly total vehicle sales in USA. The hybrid data contain both sentiment scores of tweets and stock market values. In addition, seasonal factors of monthly total vehicle sales are employed to deseasonalizing both monthly total vehicle sales and three types of input data. The time series models include the naïve model, the exponential smoothing model, the autoregressive integrated moving average model, the seasonal autoregressive integrated moving average model, and backpropagation neural networks and LSSVR with time series models. The numerical results indicate that using hybrid data with deseasonalizing procedures by the LSSVR models can obtain more accurate results than other models with different data. Thus, both social media data and stock values are essential to forecast monthly total vehicle sales; and deseasonalizing procedures can improve forecasting accuracy in predicting monthly total vehicle sales.