Predict total sales for every product and store in the next month

In this competition you will work with a challenging time-series dataset consisting of daily sales data, kindly provided by one of the largest Russian software firms - 1C Company.

Submissions are evaluated by root mean squared error (RMSE). True target values are clipped into [0,20] range.

For each id in the test set, you must predict a total number of sales. The file should contain a header and have the following format:

ID,item_cnt_month 0,0.5 1,0.5 2,0.5 3,0.5 etc.

https://www.kaggle.com/c/competitive-data-science-predict-future-sales/overview

You are provided with daily historical sales data. The task is to forecast the total amount of products sold in every shop for the test set. Note that the list of shops and products slightly changes every month. Creating a robust model that can handle such situations is part of the challenge.

• sales_train.csv - the training set. Daily historical data from January 2013 to October 2015.
• test.csv - the test set. You need to forecast the sales for these shops and products for November 2015.
• sample_submission.csv - a sample submission file in the correct format.
• items.csv - supplemental information about the items/products.
• item_categories.csv - supplemental information about the items categories.
• shops.csv- supplemental information about the shops.

• ID - an Id that represents a (Shop, Item) tuple within the test set
• shop_id - unique identifier of a shop
• item_id - unique identifier of a product
• item_category_id - unique identifier of item category
• item_cnt_day - number of products sold. You are predicting a monthly amount of this measure
• item_price - current price of an item
• date - date in format dd/mm/yyyy
• date_block_num - a consecutive month number, used for convenience. January 2013 is 0, February 2013 is 1,..., October 2015 is 33
• item_name - name of item
• shop_name - name of shop
• item_category_name - name of item category