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pnnl/PHANTOM

RESOURCE

Publicly Accessible Repository
https://github.com/pnnl/PHANTOM
https://doi.org/10.11578/dc.20230314.5

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Abstract

A topology-based anomaly detection tool for temporal varying data. The input is json or CSV formatted data with a timestamp field identified. The algorithm computes vectorizations (using a vectorization config file) of time windows of data and computes a topological measure of anomalousness. The output is a sequence of anomaly scores for each time window
Developers:
Seppala, Garret [1] ;  Central, PNNL Developer [2] ;  Purvine, Emilie [1] ;  Nowak, Kathleen [3]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Pacific Northwest National Laboratory
  3. CBP - DHS
Release Date:
2023-03-14
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Licenses:
BSD 2-clause "Simplified" License
Sponsoring Org.:
Code ID:
102608
Site Accession Number:
Battelle IPID 32440-E
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/pnnl/PHANTOM
https://doi.org/10.11578/dc.20230314.5

SAVE / SHARE



PNNL Badge

Citation Formats

Seppala, Garret, Central, PNNL Developer, Purvine, Emilie, and Nowak, Kathleen. pnnl/PHANTOM. Computer Software. https://github.com/pnnl/PHANTOM. USDOE. 14 Mar. 2023. Web. doi:10.11578/dc.20230314.5.
Seppala, Garret, Central, PNNL Developer, Purvine, Emilie, & Nowak, Kathleen. (2023, March 14). pnnl/PHANTOM. [Computer software]. https://github.com/pnnl/PHANTOM. https://doi.org/10.11578/dc.20230314.5.
Seppala, Garret, Central, PNNL Developer, Purvine, Emilie, and Nowak, Kathleen. "pnnl/PHANTOM." Computer software. March 14, 2023. https://github.com/pnnl/PHANTOM. https://doi.org/10.11578/dc.20230314.5.
@misc{ doecode_102608,
title = {pnnl/PHANTOM},
author = {Seppala, Garret and Central, PNNL Developer and Purvine, Emilie and Nowak, Kathleen},
abstractNote = {A topology-based anomaly detection tool for temporal varying data. The input is json or CSV formatted data with a timestamp field identified. The algorithm computes vectorizations (using a vectorization config file) of time windows of data and computes a topological measure of anomalousness. The output is a sequence of anomaly scores for each time window},
doi = {10.11578/dc.20230314.5},
url = {https://doi.org/10.11578/dc.20230314.5},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20230314.5}},
year = {2023},
month = {mar}
}