  We took part in the Corporacion Favorita Grocery Sales Forecasting
competition hosted on Kaggle and achieved the 2nd place. In this abstract
paper, we present an overall analysis and solution to the underlying
machine-learning problem based on time series data, where major challenges are
identified and corresponding preliminary methods are proposed. Our approach is
based on the adaptation of dilated convolutional neural network for time series
forecasting. By applying this technique iteratively to batches of n examples, a
big amount of time series data can be eventually processed with a decent speed
and accuracy. We hope this paper could serve, to some extent, as a review and
guideline of the time series forecasting benchmark, inspiring further attempts
and researches.
