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ISETS: Incremental Shapelet Extraction from Streaming Time Series
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Datasets ISETS May 15, 2019
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Incremental Shapelet Extraction from Streaming Time Series

-Jingwei ZUO, Karine ZEITOUNI, Yehia TAHER

DAVID Lab, University of Versailles Saint-Quentin, Unverisy of Paris-Sacaly

This web application is intended to provide users an intuitive understanding about the feature extraction process in a combined context of Time Series and Data Streams. With ISETS, users can monitor the occurence of Concept Drift and the Shapelet Ranking at different Time Points.

Project Page: Incremental and Adaptive Feature Exploration over Time Series Stream

Demo 1 -> ISETS Tutorial

Demo 2 ->ISETS and Adaptive Features

Configurations (demo)

  • Input File: the name should be end with "Train.csv"
  • dataset_folder: in each file, change the location of the datasets in the background. The selected input file will be saved/uploaded into this folder.
  • Data Augmentation: refer to preprocessing/ As Shapelet-based methods (e.g., SMAP) are noise resistant, we put randomly the noise of random durations into the original TS data to augment the data volume.

####Web application (demo)

  • main program, a web application based on Flask and Bokeh
  • the program for adaptive shapelet extraction and Concept Drift detection
  • show the adaptive shapelets in the web interface
  • show in real time the input TS instances in the stream

###Core algorithms

  • utils/: the repository which contains the basic file operations and similairty measure functions
  • the caching mechanism including the computation of Matrix Profile for cached instance
  • Shapelet extraction on MAtrix Profile
  • the loss computation and the Concept Drift detection on TS Stream
  • adaptive_features/ Concept Drift detection and adaptive feature extraction
  • ISMAP/ incremental Shapelet extraction on MAtrix Profile
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