Context
This dataset is intended for anybody who wants to practice and improve their time series skills.
Content
This dataset contains simulated time series data covering 10 years (2010-2019). The features include date, store id, product id and number sold. The train.csv covers the years 2010-2018 and the test.csv covers 2019 only. The are 7 unique stores and 10 unique products. The are no null values. The objective is to predict the number sold feature in the test.csv.
I created this time series data using multiple features including various long term trends, year-long seasonality patterns, weekday/weekend effects and noise. Moreover, the products and the stores are supposed to be weakly correlated.
Assessment
To compare with other peoples solutions, I recommend using the MAPE (Mean Absolute Percentage Error) metric.