Yuchen Tian
Yiqun Liu
Min Zhang
ABSTRACT
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
- H. Choi and H. Varian. Predicting the present with google trends. Technical report, Google Inc., 2009.Google Scholar
- E. Sadikov, J. Madhavan, L. Wang, and A. Halevy. Clustering query refinements by user intent. In Proceedings of the 19th World Wide Web Conference, 2010. Google ScholarDigital Library
- Peter R. Winters. Forecasting sales by exponentially weighted moving averages. Management Science, Vol.6(No.3):pp. 324--342, April 1960.Google ScholarDigital Library
Index Terms
- Incorporating seasonal time series analysis with search behavior information in sales forecasting
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