TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection
Time series anomaly detection aims to identify unusual patterns in data or deviations from systems’ expected behavior. The re-construction-based methods are the mainstream in this task, which exploit learning point-wise representation. However, the unlabeled anomaly points in training data may cause these reconstruction-based methods to learn and reconstruct anomalous data, resulting in the challenge of capturing normal patterns. In this paper, we propose a time series anomaly detection method based on implicit neural representation (INR) reconstruction, named TSINR, to address this challenge. Due to the property of spectral bias, TSINR enables prioritizing low-frequency signals and exhibiting poorer performance on high-frequency abnormal data. Specifically, we adopt INR to parameterize time series data as continuous functions and employ a transformer-based architecture to predict the INR of given data. As a result, the proposed TSINR method achieves the advantage of capturing the temporal continuity and thus is more sensitive to discontinuous anomaly data. In addition, we further design a novel continuous function to learn inter- and intra-variable information, and leverage a pre-trained large language model to amplify the intense fluctuations in anomalies. Extensive experiments demonstrate that TSINR achieves superior overall performance on both univariate and multivariate time series anomaly detection benchmark compared to other state-of-the-art reconstruction-based methods.
Please install Python>=3.8 and install the requirements via:
pip install -r requirements.txt
Please download the benchmarks from TimesNet and store the data in ./all_datasets.
Then please run the TSINR method with following command by choosing the configuration from ./cfgs:
CUDA_VISIBLE_DEVICES=0 python run_trainer.py --cfg <path to cfg>
For example:
CUDA_VISIBLE_DEVICES=0 python run_trainer.py --cfg ./cfgs/MSL.yaml
Please download the ucr dataset from here and store the data in ./all_datasets.
Then please run the experiments with the following command:
CUDA_VISIBLE_DEVICES=0 python run_trainer_ucr.py --cfg ./cfgs/UCR.yaml
