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.
- 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
- 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