Awesome
Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection
This is the implementation of ARCUS published in TheWebConf 2024 [paper]
1. Overview
In light of the remarkable advancements made in time-series anomaly detection (TSAD), recent emphasis has been placed on exploiting the frequency domain as well as the time domain to address the difficulties in precisely detecting pattern-wise anomalies. However, in terms of anomaly scores, the window granularity of the frequency domain is inherently distinct from the data-point granularity of the time domain. Owing to this discrepancy, the anomaly information in the frequency domain has not been utilized to its full potential for TSAD. In this paper, we propose a TSAD framework, Dual-TF, that simultaneously uses both the time and frequency domains while breaking the time-frequency granularity discrepancy. To this end, our framework employs nested-sliding windows, with the outer and inner windows responsible for the time and frequency domains, respectively, and aligns the anomaly scores of the two domains. As a result of the high resolution of the aligned scores, the boundaries of pattern-based anomalies can be identified more precisely. In six benchmark datasets, our framework outperforms state-of-the-art methods by 12.0–147%, as demonstrated by experimental results.
2. Public Datasets
| Name | # Applications | # Train | # Test | Entity×Dimension | # Point Anomaly (Ratio) | # Pattern Anomaly (Ratio) | Source |
|---|---|---|---|---|---|---|---|
| TODS(Point) | Synthetic | 20,000 | 5,000 | 2 × 1 | 250 (100%) | 0 (0%) | link |
| TODS(Pattern) | Synthetic | 20,000 | 5,000 | 3 × 1 | 0 (0%) | 250 (100%) | link |
| ASD | Server Monitoring | 8,527 | 4,320 | 12 × 19 | 0 (0%) | 199 (100%) | link |
| ECG | Medical Checkup | 6,995 | 2,851 | 9 × 2 | 0 (0%) | 208 (100%) | link |
| PSM | Server Monitoring | 132,481 | 87,841 | 1 × 25 | 16 (0.07%) | 24,365 (99.93%) | link |
| Company A | Server Monitoring | 21,600 | 13,302 | 3 × 8 | 10 (8.53%) | 104 (91.47%) | Private |
3. Requirements and Installations
- Node.js: 16.13.2+
- Anaconda 4 or Miniconda 3
- Python 3.8.12 (Recommend Anaconda)
- Ubuntu 16.04.3 LTS
- pytorch >= 1.13.1
4. Configuration
Dual-TF was implemented in Python 3.8.12.
- Edit main.py and main_freq.py files to set experiment parameters (dataset, seq_length, gpu_id(e.g. 0,1,2,3,4,5), etc.)
python3 main.py
python3 main_freq.py
5. How to run
- Parameter options
--dataset: the name of dataset (string)
--seq_length: the size of a window (integer)
--step: the size of a slide (integer)
--input_c: the datasets' dimension (integer)
--output_c: the datasets' dimension (integer)
--form: the data form for TODS dataset (string, e.g. point, context, shaplet, seasonal, trend)
--data_num: the number of dataset. (integer)
- At current directory which has all source codes, run main.py with parameters as follows.
- dataset: {TODS, ASD, ECG, PSM, CompanyA}
- seed: {0, 1, 2} # seed for 3-fold cross validation.
- gpu_id: an integer for gpu id.
- seq_length: an integer for outer window length
- nest_length: an integer for inner window length
e.g.) python3 main.py --gpu_id 6 --form seasonal --seq_length 50 --dataset NeurIPSTS --batch_size 4
6. Licence
Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection © 2023 by Youngeun Nam is licensed under CC BY-NC-SA 4.0. To view a copy of this license, visit (http://creativecommons.org/licenses/by-nc-sa/4.0/)