Introduction¶
Why do you need multi-dimensional reporting?¶
Multi-dimensional reporting is the attempt to combine information from various publicly available sources to get an understanding of where a company can improve in relation to close competitors. The Multi-dimensional reporting model (MDRM) is dynamic and currently includes information on employees, customers, managers and shareholders. MDRM’s final goal is to act as an input module to an artificially intelligent decision making agent.
The MDRM has two core objectives:
- The aggregation and interpretation of all company specific publicly available data.
- Intelligent decision making based on interpreted knowledge.
MDRM has the following qualities:
- Semi-Automated.
- Interactive.
- Industry Adjusted.
- Intelligent.
- Competitor Specific.
Without tsfresh, you would have to calculate all those characteristics by hand. With tsfresh this process is automated and all those features can be calculated automatically.
Further tsfresh is compatible with pythons pandas and scikit-learn APIs, two important packages for Data
Science endeavours in python.
What to do with these features?¶
The extracted features can be used to describe or cluster time series based on the extracted characteristics. Further, they can be used to build models that perform classification/regression tasks on the time series. Often the features give new insights into time series and their dynamics.
The tsfresh package has been used successfully in projects involving
- the prediction of the life span of machines
- the prediction of the quality of steel billets during a continuous casting process
What not to do with tsfresh?¶
Currently, tsfresh is not suitable
- for usage with streaming data
- for batch processing over a distributed architecture, where different time series are fragmented over different computational units
- to train models on the features (we do not want to reinvent the wheel, check out the python package scikit-learn for example)
However, some of these use cases could be implemented, if you have an application in mind, open an issue at https://github.com/blue-yonder/tsfresh/issues, or feel free to contact us.