The Ionosphere branch introduced tsfresh to the Skyline stack to enable the creation of feature profiles for time series that the user deems to be not anomalous. https://github.com/blue-yonder/tsfresh/
See Development - Ionosphere for the long trail that lead to tsfresh.
Skyline tsfresh fork
Due to the addition of new algorithms/features and modifications to tsfresh, the original blue-yonder/tsfresh package can no longer be run on any of the tsfresh releases since v0.5.0 with Skyline to achieve consistent results on features extraction. Therefore Skyline runs a modified fork of the tsfresh. This modified fork maintains the features extracted at v0.4.0 but moves this tsfresh forked version forward in line with blue-yonder/tsfresh in terms of tsfresh internals and dependencies, etc.
In this fork:
New features added to blue-yonder/tsfresh are disabled
Original methods for features are maintained even if they are changed in blue-yonder/tsfresh
these branches/versions are tested against the tests/baseline/tsfresh_features_test.py, which was removed from blue-yonder/tsfresh in v0.7.0 but has been readded to this fork. These branches/versions are only tested via the Skyline build tests, they are not tested against the tsfresh tests. Seeing as this fork follows the blue-yonder/tsfresh versions and retrospectively makes backwards compatible changes to the settings and feature_calculators.py which work with the Skyline tests. Therefore these changes are not currently backported to the tsfresh tests themselves and the tsfresh tests will fail if run against any of theses branches.
tsfresh and Graphite integration
Skyline needs to tie Graphite, Redis and tsfresh together. However these is fairly straight forward really, but to save any others having to reverse engineer the process the skyline/tsfresh_features/scripts are written is a generic type of way that does not require downloading Skyline, they should run standalone so that others can use them if they want some simple Graphite -> tsfresh feature extraction capabilities.
Assign a Graphite single tiemseries metric csv file to tsfresh to process and calculate the features for.
- param path_to_your_graphite_csv:
the full path and filename to your Graphite single metric time series file saved from a Graphite request with &format=csv
- type path_to_your_graphite_csv:
- param pytz_tz:
[OPTIONAL] defaults to UTC or pass as your pytz timezone string. For a list of all pytz timezone strings see https://github.com/earthgecko/skyline/blob/ionosphere/docs/development/pytz.rst and find yours.
- type string:
Run the script with, a virtualenv example is shown but you can run just with Python-3.8 from wherever you save the script:
bin/python3.8 tsfresh_features/scripts/tsfresh_graphite_csv path_to_your_graphite_csv [pytz_timezone]
Where path_to_your_graphite_csv.csv is a single metric time series that has been from retrieved from Graphite with the &format=csv request parameter and saved to a file.
The single metric time series could be the result of a graphite function on multiple time series, as long as it is a single time series. This does not handle multiple time series data, meaning a Graphite csv with more than one data set will not be suitable for this script.
This will output 2 files:
path_to_your_graphite_csv.features.csv (default tsfresh column wise format)
path_to_your_graphite_csv.features.transposed.csv (human friendly row wise format) you look at this csv :)
Your time series features.
Please note that if your time series are recorded in a daylight savings time zone, this has not been tested with DST changes.