  Sales forecasting plays a prominent role in business planning and business
strategy. The value and importance of advance information is a cornerstone of
planning activity, and a well-set forecast goal can guide sale-force more
efficiently. In this paper CPU sales forecasting of Intel Corporation, a
multinational semiconductor industry, was considered. Past sale, future
booking, exchange rates, Gross domestic product (GDP) forecasting, seasonality
and other indicators were innovatively incorporated into the quantitative
modeling. Benefit from the recent advances in computation power and software
development, millions of models built upon multiple regressions, time series
analysis, random forest and boosting tree were executed in parallel. The models
with smaller validation errors were selected to form the ensemble model. To
better capture the distinct characteristics, forecasting models were
implemented at lead time and lines of business level. The moving windows
validation process automatically selected the models which closely represent
current market condition. The weekly cadence forecasting schema allowed the
model to response effectively to market fluctuation. Generic variable
importance analysis was also developed to increase the model interpretability.
Rather than assuming fixed distribution, this non-parametric permutation
variable importance analysis provided a general framework across methods to
evaluate the variable importance. This variable importance framework can
further extend to classification problem by modifying the mean absolute
percentage error(MAPE) into misclassify error. Please find the demo code at :
https://github.com/qx0731/ensemble_forecast_methods
