Incorporating seasonal time series analysis with search behavior information in sales forecasting

We consider the problem of predicting monthly auto sales in mainland China. First, we design an algorithm using click-through and query reformulation information to cluster related queries and count their frequencies on monthly-basis. By introducing Exponentially Weighted Moving Averages (EWMA) model, we measure the seasonal impact on the sales trend. Two features are combined using linear regression. The experiment shows that our model is effective with high accuracy and outperforms conventional forecasting models.1