This project aims to find the outliers, trends and periodicity in the cell phone-generated activity data. Taking time series analysis as the main body, data cleaning and merging are preprocess of this project.
Time series, events detection, trends detection, periods detection
In order to check the day to day variances for two kinds of visitors, I perform time series analysis of daily counts of visitors in a certain area, which is an active zone in the city.
The cell phone-generated activity data is produced by AirSage. AirSage is founded in 2000 and it aimed to transform wireless network signaling data into digital mobility information. This dataset is kind of yellow and it is prohibited to distribute or duplicate it. The data contains information of daily activity counts for visitors in certain zone for a month, and there are two kinds of visitors, which are long term visitors and short term visitors.