You are provided with historical sales data for 45 Walmart stores located in different regions. Each store contains a number of departments, and you are tasked with predicting the department-wide sales for each store.
You have been provided with sales data for 111 products whose sales may be affected by the weather (such as milk, bread, umbrellas, etc.). These 111 products are sold in stores at 45 different Walmart locations. The competition task is to predict the amount of each product sold around the time of major weather events.
You are provided with historical sales data for 1,115 Rossmann stores. The task is to forecast the “Sales” column for the test set. Note that some stores in the dataset were temporarily closed for refurbishment.
The training dataset consists of approximately 145k time series. Each of these time series represent a number of daily views of a different Wikipedia article. The final ranking of the competition will be based on predictions of daily views for each article in the dataset.
In this competition, you will be predicting the unit sales for thousands of items sold at different Favorita stores located in Ecuador. The training data includes dates, store and item information, whether that item was being promoted, as well as the unit sales.
In this competition, you are provided a time-series forecasting problem centered around restaurant visitors. You will use the reservations, visits, and other information from these sites to forecast future restaurant visitor totals on a given date.
In this challenge, you will be predicting the daily number of confirmed COVID19 cases in various locations across the world, as well as the number of resulting fatalities, for future dates. This latest challenge includes US state county data.
In this competition, you’ll use your machine learning expertise to forecast short term returns in 14 popular cryptocurrencies. We have amassed a dataset of millions of rows of high-frequency market data dating back to 2018 which you can use to build your model.