## Features
- Two benchmark suites
- 1) Fully synthetic benchmark suite
- 2) Semi-realistic benchmark suite
+ 1) Fully synthetic benchmark suite
+ 2) Semi-realistic benchmark suite
- Only multivariate sequences
-- Expressive and compact
+- Expressive and compact
- Full knowledge about the sequences and the anomalies
- Compatible to [TimeEval](https://github.com/HPI-Information-Systems/TimeEval)
+- Online Dataset visualizer: https://2er0.github.io/mTADS
### Fully synthetic benchmark suite samples
@@ -25,12 +31,11 @@ Benchmark Suites for Anomaly Detection on Multivariate Time Series
-
## Usage
### Sequence iterator
-Use a file iterator to load the sequences or the build in loading functionality with caching capabilities.
+Use a file iterator to load the sequences or the build in loading functionality with caching capabilities.
```python
from loading import load_all_stored_datasets
@@ -41,20 +46,27 @@ for gen in generator:
...
```
-- `group` contains the name of the loaded sequence and may contain `no-anomaly` in the end, which means that the training sequences does not contain any anomaly and is for unsupervised and semi-supervised algorithms.
+- `group` contains the name of the loaded sequence and may contain `no-anomaly` in the end, which means that the
+ training sequences does not contain any anomaly and is for unsupervised and semi-supervised algorithms.
- `parameters` contains the generation configuration and varies between the GutenTAG generated ones and the added.
-- `train_sequence` is a Pandas DataFrame containing the sequence for training with the column structure `timestamp,value-0,value-1,…,is_anomaly` and is fully compatible to GutenTAG and TimeEval
-- `test_sequence` is a Pandas DataFrame containing the sequence for testing with the column structure `timestamp,value-0,value-1,…,is_anomaly` and is fully compatible to GutenTAG and TimeEval
+- `train_sequence` is a Pandas DataFrame containing the sequence for training with the column
+ structure `timestamp,value-0,value-1,…,is_anomaly` and is fully compatible to GutenTAG and TimeEval
+- `test_sequence` is a Pandas DataFrame containing the sequence for testing with the column
+ structure `timestamp,value-0,value-1,…,is_anomaly` and is fully compatible to GutenTAG and TimeEval
### Compatibility note
-The semi-realistic benchmark suite is not fully compatible with the TimeEval framework because the sequences include additional information. They include in the which channel contains the anomaly and the magnitude of the artificial change. This information is automatically filtered out by using the provided loader in `loading.py`.
+The semi-realistic benchmark suite is not fully compatible with the TimeEval framework because the sequences include
+additional information. They include in the which channel contains the anomaly and the magnitude of the artificial
+change. This information is automatically filtered out by using the provided loader in `loading.py`.
---
### Generate sequences
-Executing the `main.py` file generates the sequences that are not covered via the [GutenTAG framework](https://github.com/HPI-Information-Systems/GutenTAG):
+Executing the `main.py` file generates the sequences that are not covered via
+the [GutenTAG framework](https://github.com/HPI-Information-Systems/GutenTAG):
+
```shell
usage: main.py [-h] [--suite {FSB,SRB}] [--save SAVE] [--plot PLOT] [--interactive INTERACTIVE] [--remove_orphan_only REMOVE_ORPHAN_ONLY]
@@ -70,25 +82,30 @@ optional arguments:
```
Generate the GutenTAG framework covered sequences:
+
```shell
pip install timeeval-gutenTAG
gutenTAG --config-yaml GutenTAG-config.yaml --seed 11
```
+
It is required to have GutenTAG installed on the system to generate the sequences.
## Generate new sequences
### Changing parameters
-To generate new sequences that are covered by the GutenTAG framework, then please follow their documentation and update the configuration in [GutenTAG-config.yaml](GutenTAG-config.yaml) accordingly and generate the sequences with the command above.
+To generate new sequences that are covered by the GutenTAG framework, then please follow their documentation and update
+the configuration in [GutenTAG-config.yaml](GutenTAG-config.yaml) accordingly and generate the sequences with the
+command above.
-To generate new sequences that ere not covered by the GutenTAG framework, then update parameterization in the file [generators.py](generators.py) and execute the [main.py](main.py) after. Be aware that not all generators configured in the `generator.py` file are generated. They need to be added to the `main.py` file to be generated.
+To generate new sequences that ere not covered by the GutenTAG framework, then update parameterization in the
+file [generators.py](generators.py) and execute the [main.py](main.py) after. Be aware that not all generators
+configured in the `generator.py` file are generated. They need to be added to the `main.py` file to be generated.
## Citation
If you use mTADS in your project or research, please cite our demonstration paper:
-
```bibtex
@article{Baumgartner2024TmTADS,
title = {mTADS: Multivariate Time Series Anomaly Detection Benchmark Suites},
@@ -96,3 +113,12 @@ If you use mTADS in your project or research, please cite our demonstration pape
date = {2024}
}
```
+
+## Contributors
+
+
+
+ 
+
+
+
+