Time Series Feature Extraction for industrial big data (IIoT) applications

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Headings…
Time Series Feature Extraction for industrial big data (IIoT) applications
Feature Extraction by Distributed and Parallel means for industrial big dat
Motivation — Why Feature extraction is necessary?
What is Feature Extraction from Time series data and Why it is important?
Feature Extraction and Selection Process
Tsfresh and its usage
Modeling a practical use-case
Feature Significance and Relevance
Classification
Conclusion

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Time Series Feature Extraction for industrial big data (IIoT) applications
Feature Extraction by Distributed and Parallel means for industrial big data applications
Feature extraction remains one of the most preliminary steps in machine learning algorithms to identify strong and weak relevant attributes.
While many feature extraction algorithms are used during Feature Engineering for standard classification and regression problems, the problem turns increasing difficult for time series classification and regression problems where each label or regression target is associated with several time series and meta-information simultaneously.
Trust me such scenarios are quite common with huge datasets obtained from industrial heavy manufacturing equipments, machinery, IoT which often go under maintenance or exibit production line optimization demonstrating different success and failure metrics in different time series.
The main objective in this blog remains to understand the procedures in extracting relevant features from multiple time series and model with real dataset.
Evaluate the importance of time series feature extraction for classification and regression problems.
What is Feature Extraction from Time series data and Why it is important?
Feature extraction controls selecting the important and useful features, by eliminating redundant features and noise from the system, to yield the best predicted output.
Extract characteristics feature from time series, such as min, max, average, percentile or other mathematical derivations.
Consolidate feature extraction and selection process from distributed heterogeneous sources of information lying on different time-series scale for predicting the target output.
Allow time series clustering (un-supervised learning) from extracted features based on its relevancy

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