Overview
mobts is a Python package for working with mobility time series data.
It is mainly aimed for count data collected from urban mobility sensors, such as bicycle counters, traffic counters, or similar counting systems.
The multi-stage pipeline
The package currently focuses on two main tasks:
Preprocessing - prepares raw data for analysis: standardizing the format, detecting suspicious or unreliable observations (measurement errors, sensor dropouts, implausible spikes), and replacing them with
NaN.Imputation - fills in missing values, whether they were already missing or were just flagged by preprocessing, using time-series and donor-based estimation methods.
Running preprocessing before imputation isn’t required, but it’s recommended: separating a “true zero” observation from a “the sensor was broken” observation gives imputation a cleaner time-series to work with.
Why mobts
Raw mobility counter data is rarely clean. Common problems include:
missing observations
suspicious zero values
sensor failures
extreme, implausible values
mobts provides tools to detect these issues and produce a complete, reliable time series from imperfect raw data.
Where to go next
New to the package? Start with Installation and Quickstart for a minimal working example.
Want more detail on either stage? See Preprocessing and Imputation.
Every default threshold or configuration used by both stages can be overridden — see Configuration.