SEAnet is a novel architecture especially designed for data series representation learning (DEA).
Codes were developed and tested under Linux environment.
- Compile Coconut Sampling
cd lib/
make
- Add a configuration file
An example configuration for SEAnet is given in conf/example.json. Two fields with TO_BE_CHANGED are required to get changed.
database_path: indicates the dataset to be indexed
query_path: indicates the query set
Other fields could be left by default. Please refer to util/conf.py for all possible configurations.
- Train SEAnet
python run.py -C conf/example.json
The indexing and query answering of DEA is in https://github.com/qtwang/isax-modularized
@inproceedings{kdd21-Wang-SEAnet,
author = {Wang, Qitong and
Palpanas, Themis},
title = {Deep Learning Embeddings for Data Series Similarity Search},
booktitle = {{KDD} '21: The 27th {ACM} {SIGKDD} Conference on Knowledge Discovery
and Data Mining, Virtual Event, Singapore, August 14-18, 2021},
publisher = {{ACM}},
year = {2021},
url = {https://doi.org/10.1145/3447548.3467317},
doi = {10.1145/3447548.3467317},
timestamp = {Thu, 05 Aug 2021 09:46:47 +0800}
}