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https://github.com/HPI-Information-Systems/DADS
Distributed detection of sequential anomalies in univariate time series
https://github.com/HPI-Information-Systems/DADS
Last synced: 15 days ago
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Distributed detection of sequential anomalies in univariate time series
- Host: GitHub
- URL: https://github.com/HPI-Information-Systems/DADS
- Owner: HPI-Information-Systems
- Created: 2020-07-20T13:21:12.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-01-05T10:43:43.000Z (almost 4 years ago)
- Last Synced: 2024-08-01T16:26:55.641Z (3 months ago)
- Language: Java
- Homepage:
- Size: 838 KB
- Stars: 9
- Watchers: 8
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Distributed Detection of Sequential Anomalies in Univariate Time Series
Source code for the Distributed Anomaly Detection System (DADS)
This algorithm is based on the [Series2Graph](http://helios.mi.parisdescartes.fr/~themisp/series2graph/) algorithm, that was published in `P. Boniol and T. Palpanas, Series2Graph: Graph-based Subsequence Anomaly Detection in Time Series, PVLDB (2020)`.
## Usage
### Building
Requirements:
- JDK 8 (Java)
- Maven
```sh
mvn package
```
You can skip the tests with `-DskipTests`.
The created fat JAR is located in the `target`-folder.
### Notes
The algorithm only accepts binary input files without timestamps and ordered double values.
To convert a text file, use the Python script in [`script/sequence-converter/convert.py`](./script/sequence-converter/convert.py).
#### Example Conversion
Input file:
```text
0.333443
0.466437
0.474765
```
Converting:
```shell
python3 script/sequence-converter/convert.py --input input.txt --output output.bin
```
### Running
Requirements:
- JRE 8 (Java)
```sh
java -jar .jar master --host localhost --port 7788 --min-slaves 0 \
--sequence \
--sub-sequence-length 50 --intersection-segments 50 \
--query-length 75 --convolution-size 16 \
--output ./results.txt --no-statistics
```