Package: rtm-tsfresh
tsfreshThis repository contains theTSFRESHpython package. The abbreviation stands for"Time Series Feature extraction based on scalable hypothesis tests".The package provides systematic time-series feature extraction by combining established algorithms from statistics, time-series analysis, signal processing, and nonlinear dynamics with a robust feature selection algorithm. In this context, the termtime-seriesis interpreted in the broadest possible sense, such that any types of sampled data or even event sequences can be characterised.Spend less time on feature engineeringData Scientists often spend most of their time either cleaning data or building features.
While we cannot change the first thing, the second can be automated.TSFRESHfrees your time spent on building features by extracting them automatically.
Hence, you have more time to study the newest deep learning paper, read hacker news or build better models.Automatic extraction of 100s of featuresTSFRESHautomatically extracts 100s of features from time series.
Those features describe basic characteristics of the time series such as the number of peaks, the average or maximal value or more complex features such as the time reversal symmetry statistic.The set of features can then be used to construct statistical or machine learning models on the time series to be used for example in regression or
classification tasks.Forget irrelevant featuresTime series often contain noise, redundancies or irrelevant information.
As a result most of the extracted features will not be useful for the machine learning task at hand.To avoid extracting irrelevant features, theTSFRESHpackage has a built-in filtering procedure.
This filtering procedure evaluates the explaining power and importance of each characteristic for the regression or classification tasks at hand.It is based on the well developed theory of hypothesis testing and uses a multiple test procedure.
As a result the filtering process mathematically controls the percentage of irrelevant extracted features.TheTSFRESHpackage is described in the following open access paper:Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018).Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh -- A Python package).Neurocomputing 307 (2018) 72-77,doi: 10.1016/j.neucom.2018.03.067.The FRESH algorithm is described in the following whitepaper:Christ, M., Kempa-Liehr, A.W., and Feindt, M. (2017).Distributed and parallel time series feature extraction for industrial big data applications.ArXiv e-print 1610.07717,https://arxiv.org/abs/1610.07717.Due to the fact that tsfresh basically provides time-series feature extraction for free, you can now concentrate on engineering new time-series,
like e.g. differences of signals from synchronous measurements, which provide even better time-series features:Kempa-Liehr, A.W., Oram, J., Wong, A., Finch, M., Besier, T. (2020).Feature engineering workflow for activity recognition from synchronized inertial measurement units.In: Pattern Recognition. ACPR 2019. Ed. by M. Cree et al. Vol. 1180. Communications in Computer and Information Science (CCIS).
Singapore: Springer 2020, 223–231.doi: 10.1007/978-981-15-3651-9_20.Systematic time-series features engineering allows to work with time-series samples of different lengths, because every time-series is projected
into a well-defined feature space. This approach allows the design of robust machine learning algorithms in applications with missing data.Kennedy, A., Gemma, N., Rattenbury, N., Kempa-Liehr, A.W. (2021).Modelling the projected separation of microlensing events using systematic time-series feature engineering.Astronomy and Computing 35.100460 (2021), 1–14,doi: 10.1016/j.ascom.2021.100460Natural language processing of written texts is an example of applying systematic time-series feature engineering to event sequences,
which is described in the following open access paper:Tang, Y., Blincoe, K., Kempa-Liehr, A.W. (2020).Enriching Feature Engineering for Short Text Samples by Language Time Series Analysis.EPJ Data Science 9.26 (2020), 1–59.doi: 10.1140/epjds/s13688-020-00244-9Advantages of tsfreshTSFRESHhas several selling points, for exampleit is field testedit is unit testedthe filtering process is statistically/mathematically correctit has a comprehensive documentationit is compatible with sklearn, pandas and numpyit allows anyone to easily add their favorite featuresit both runs on your local machine or even on a clusterNext stepsIf you are interested in the technical workings, go to see our comprehensive Read-The-Docs documentation athttp://tsfresh.readthedocs.io.The algorithm, especially the filtering part are also described in the paper mentioned above.If you have some questions or feedback you can find the developers in thegitter chatroom.We appreciate any contributions, if you are interested in helping us to makeTSFRESHthe biggest archive of feature extraction methods in python, just head over to ourHow-To-Contributeinstructions.If you want to try outtsfreshquickly or if you want to integrate it into your workflow, we also have a docker image available:docker pull nbraun/tsfreshAcknowledgementsThe research and development ofTSFRESHwas funded in part by the German Federal Ministry of Education and Research under grant number 01IS14004 (project iPRODICT).