ISETS

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/TS_stream_preprocess.py. 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)

• ISETS_webapp.py: main program, a web application based on Flask and Bokeh
• ISETS_webbackend.py: the program for adaptive shapelet extraction and Concept Drift detection
• draw_adaptive_shapelets.py: show the adaptive shapelets in the web interface
• draw_TS_Stream.py: 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
• memory_block.py: the caching mechanism including the computation of Matrix Profile for cached instance
• SMAP_block.py: Shapelet extraction on MAtrix Profile
• evaluation_block.py: the loss computation and the Concept Drift detection on TS Stream
• adaptive_features/adaptive_features.py: Concept Drift detection and adaptive feature extraction
• ISMAP/ISAMP.py: incremental Shapelet extraction on MAtrix Profile

Citation

If you find this repository useful in your research, please consider citing the following paper:

@inproceedings{zuo2019isets,
  title={ISETS: Incremental Shapelet Extraction from Time Series Stream},
  author={Zuo, Jingwei and Zeitouni, Karine and Taher, Yehia},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={790--793},
  year={2019},
  organization={Springer}
}