KDD Cup 2021 Multi-dataset Time Series Anomaly Detection 5th place solution

This repository provides the code for the KDD Cup 2021: Multi-dataset Time Series Anomaly Detection 5th place solution.

Please watch the video for a brief explanation of the solution.

Requirement

• python 3.8+
• numpy
• pandas
• tqdm
• stumpy

If you have a CUDA-capable GPU and have installed CUDA toolkit, you can accelerate the computation. If not, please turn off the GPU usage option in code.py, i.e., use_gpu = False.

Usage

1. Download dataset and place the unzipped *.txt files in the dataset/phase2 directory.
2. Run 20210601/code.py to get the detection result 20210601/result.csv.

It takes several days to run because it computes matrix profile with different subsequence lengths for each of the 250 time series.

There are two submission codes in this repository.

• 20210531/code.py is the final submission code, which achieves 217 / 250 = 86.8% accuracy and results in 5th place in the private leader board.
• 20210601/code.py is another code I tried to submit on the last day (June 1st), however, I failed to submit it because the time zone of the deadline was unclear (The datetime for each submission was recorded and displayed in UTC-7, but the deadline seemed to be UTC+0). It turns out that it can achieve 218 / 250 = 87.2% accuracy, which is equivalent to the 3rd place.