  Time-series forecasting is an important task in both academic and industry,
which can be applied to solve many real forecasting problems like stock,
water-supply, and sales predictions. In this paper, we study the case of
retailers' sales forecasting on Tmall|the world's leading online B2C platform.
By analyzing the data, we have two main observations, i.e., sales seasonality
after we group different groups of retails and a Tweedie distribution after we
transform the sales (target to forecast). Based on our observations, we design
two mechanisms for sales forecasting, i.e., seasonality extraction and
distribution transformation. First, we adopt Fourier decomposition to
automatically extract the seasonalities for different categories of retailers,
which can further be used as additional features for any established regression
algorithms. Second, we propose to optimize the Tweedie loss of sales after
logarithmic transformations. We apply these two mechanisms to classic
regression models, i.e., neural network and Gradient Boosting Decision Tree,
and the experimental results on Tmall dataset show that both mechanisms can
significantly improve the forecasting results.
