s2gpp 1.0.2

Algorithm for Highly Efficient Detection of Correlation Anomalies in Multivariate Time Series

Series2Graph++

Series2Graph++ (S2G++) is a time series anomaly detection algorithm based on the Series2Graph (S2G) and the DADS algorithms. S2G++ can handle multivariate time series whereas S2G and DADS can cope with only univariate time series. Moreover, S2G++ takes ideas from DADS to run distributedly in a computer cluster. S2G++ is written in Rust and leverages the actix and actix-telepathy libraries.

Quick Start

Requirements

• Rust 1.58
• openblas
• (Docker)

To have openblas available to the Rust build process, do the following on Debian (Linux):

sudo apt install build-essential gfortran libopenblas-base libopenblas-dev gcc

Installation

From source

git pull https://gitlab.hpi.de/akita/s2gpp
cd s2gpp
cargo build

Docker

The base image akita/rust-base must be available to your machine.

git pull https://gitlab.hpi.de/akita/s2gpp
cd s2gpp
docker build s2gpp .

Usage (bin)

Parameters

Pattern:

s2gpp --local-host <IP:Port> --pattern-length <Int> --latent <Int> --query-length <Int> --rate <Int> --threads <Int> --cluster-nodes <Int> --score-output-path <Path> [main --data-path <Path> | sub --mainhost <IP:Port>]

S2G++ expects one of two sub-commands with its specific parameters:

• main (The head computer in a cluster)
  • data-path (The path to the input time series)
• sub (The other computers in a cluster; only necessary in a distributed setting)
  • mainhost (The ip-address to the main computer in a cluster)

Before these sub-commands are used, general parameters must be defined:

• local-host (The ip-address with port to bind the listener on.)
• pattern-length (Size of the sliding window, independent of anomaly length, but should in the best case be larger.)
• latent (Size of latent embedding space. This space is the input for the PCA calculation afterwards.)
• query-length (Size of the sliding windows used to find anomalies (query subsequences). query-length must be >= pattern-length!)
• rate (Number of angles used to extract pattern nodes. A higher value will lead to high precision, but at the cost of increased computation time.)
• threads (Number of helper threads started besides the main thread. (min=1))
• cluster-nodes (Size of the computer cluster.)
• score-output-path (Path the score are written to.)
• column-start-idx (How many columns to skip)
• column-end-idx (Until which column to use (exclusive). Can also take negative numbers to count from the end.)
• self-correction (Whether S2G++ will correct the direction of the time embedding if too few transactions are available)

Input Format

The input format of the time series is expected to be a CSV with header. Each column represents a channel of the timeseries. Sometimes, time series files include also the labels and an index. You can skip columns with the column-start-idx / column-end-idx range pattern. It behave like Python ranges.

Usage (lib)

Cargo.toml

[dependencies]
s2gpp = "1.0.2"

your Rust app

fn some_fn(timeseries: Array2<f32>) -> Result<Array1<f32>, ()> {
  let params = s2gpp::Parameters::default();
  let anomaly_score = s2gpp::s2gpp(params, Some(timeseries))?.unwrap();
  Ok(anomaly_score)
}

Python

We have wrapped the Rust code in a Python package, that can be used without installing Rust.

Installation

PyPI

pip install s2gpp

Build with Docker

make build-docker
pip install wheels/s2gpp-*.whl

Build from Source

make install

Usage

Single Machine

from s2gpp import Series2GraphPP
import pandas as pd

ts = pd.read_csv("data/ts_0.csv").values

model = Series2GraphPP(pattern_length=100)
anomaly_scores = model.fit_predict(ts)

Distributed

from s2gpp import DistributedSeries2GraphPP
from pathlib import Path

# run on one machine
def main_node():
    dataset_path = Path("data/ts_0.csv")
  
    model = DistributedSeries2GraphPP.main(local_host="127.0.0.1:1992", n_cluster_nodes=2, pattern_length=100)
    model.fit_predict(dataset_path)

# run on other machine
def sub_node():
    model = DistributedSeries2GraphPP.sub(local_host="127.0.0.1:1993", mainhost="127.0.0.1:1992", n_cluster_nodes=2, pattern_length=100)
    model.fit_predict()

Cite

Please cite this work, when using it!

@software{Wenig_Series2Graph_2022,
  author = {Wenig, Phillip},
  month = {6},
  title = {{Series2Graph++}},
  version = {1.0.2},
  year = {2022}
}

References

[1] P. Boniol and T. Palpanas, Series2Graph: Graph-based Subsequence Anomaly Detection in Time Series, PVLDB (2020) link

[2] Schneider, J., Wenig, P. & Papenbrock, T. Distributed detection of sequential anomalies in univariate time series. The VLDB Journal 30, 579–602 (2021). link