Evaluation of Transformers for Multivariate Time-Series Anomaly Detection

Evaluation of several Transformer Architectures on Multivariate Time-Series Data.

Forked from the Time Series Library (TSlib): https://github.com/thuml/Time-Series-Library TSlib is an open-source library for deep learning researchers, especially for deep time series analysis.

Usage

1. Install Python 3.8. For convenience, execute the following command.

pip install -r requirements.txt

2. Prepare Data. You can obtain the well pre-processed datasets from [Google Drive] or [Baidu Drive], Then place the downloaded data in the folder./dataset. Here is a summary of supported datasets.

3. Train and evaluate model. We provide the experiment scripts for all benchmarks under the folder ./scripts/. You can reproduce the experiment results as the following examples:

# long-term forecast
bash ./scripts/long_term_forecast/ETT_script/TimesNet_ETTh1.sh
# short-term forecast
bash ./scripts/short_term_forecast/TimesNet_M4.sh
# imputation
bash ./scripts/imputation/ETT_script/TimesNet_ETTh1.sh
# anomaly detection
bash ./scripts/anomaly_detection/PSM/TimesNet.sh
# classification
bash ./scripts/classification/TimesNet.sh

4. Develop your own model.

• Add the model file to the folder ./models. You can follow the ./models/Transformer.py.
• Include the newly added model in the Exp_Basic.model_dict of ./exp/exp_basic.py.
• Create the corresponding scripts under the folder ./scripts.