VETime: Vision-Enhanced Zero-Shot Time Series Anomaly Detection

News: Our work has been accepted by NeurIPS 2026.

This repository contains the official PyTorch implementation of VETime, a vision-enhanced framework for zero-shot time-series anomaly detection.

Overview

Time-series anomaly detection must identify both point anomalies and long-range contextual anomalies. Existing approaches typically trade off fine-grained temporal localization against global pattern understanding. VETime addresses this trade-off by aligning temporal and visual representations and dynamically fusing the complementary information from both modalities.

The framework contains four main components:

• Traceable Image Conversion: converts time series into visual representations while preserving temporal correspondence.
• Patch-Level Temporal Alignment: aligns visual patches with the original temporal timeline.
• Anomaly Window Contrastive Learning: improves the separation of normal and anomalous windows.
• Task-Adaptive Multi-Modal Fusion: adaptively combines temporal and visual features for anomaly localization.

VETime is designed for zero-shot evaluation and supports comprehensive evaluation on the TSB-AD benchmark.

Repository Structure

VETime-main/
├── config/                         # Training, testing, and baseline scripts
├── dataset/
│   ├── dataloader.py               # Dataset loading and batch collation
│   ├── pre_image.py                # Time-series-to-image conversion
│   ├── Datasets/                   # TSB-AD-U/M data and file lists
│   └── TSB-AD-main/                # Bundled TSB-AD evaluation code
├── model/
│   ├── VETime.py                   # Main VETime model
│   ├── VTS_module.py               # Visual-temporal alignment and fusion
│   ├── Vision_encoder/             # Vision encoder components
│   └── TS_encoder/                 # Time-series encoder components
├── loss/                           # Training losses
├── evaluation/                     # Metrics and evaluation utilities
├── train.py                        # Training entry point
├── Test_TSB.py                    # TSB-AD evaluation entry point
├── requirements.txt                # Project dependencies
└── LICENSE

Installation

Requirements

• Python 3.8 or newer; experiments were tested with Python 3.11.
• PyTorch 2.3.0 or newer.
• CUDA 12.1 is recommended for GPU execution.

Setup

git clone https://github.com/yyyangcoder/VETime.git
cd VETime

conda create -n VETime python=3.11
conda activate VETime

conda install pytorch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 pytorch-cuda=12.1 -c pytorch -c nvidia
pip install -r requirements.txt

Optional baseline-specific dependencies are documented in dataset/TSB-AD-main/models/README.md.

Data and Checkpoints

VETime uses the TSB-AD benchmark. Download and place the datasets under dataset/Datasets/:

• TSB-AD-U: univariate time series.
• TSB-AD-M: multivariate time series.

The expected layout is:

dataset/Datasets/
├── TSB-AD-U/
├── TSB-AD-M/
└── File_List/

Download the pre-trained checkpoints from Hugging Face when required:

huggingface-cli download yyyang0/VETime-checkpoints --local-dir ./checkpoints

See dataset/Datasets/README.md for dataset details and file naming conventions.

Running VETime

The reusable commands are provided in config/:

# Train VETime
bash config/run_vetime_train.sh

# Evaluate VETime on TSB-AD-U
bash config/run_vetime_test.sh

# Evaluate the configured baseline models
bash config/run_baselines.sh

Before running the scripts, verify the dataset, checkpoint, and output paths in the corresponding shell script. The evaluation entry point can also be invoked directly:

python Test_TSB.py \
    --model_name VETime \
    --dataset_dir ./dataset/Datasets/TSB-AD-U \
    --save_dir ./output/metrics/uni/ \
    --device cuda:0

Evaluation Metrics

The implementation reports the following metrics for anomaly detection:

Metric	Description
VUS-PR	Volume Under Surface for precision-recall evaluation
Affiliation metrics	Event-based anomaly detection metrics
F1-T	Range-based F1 score with temporal context
Standard-F1	Point-wise F1 score

Citation

If you find VETime useful, please cite our work:

@article{yang2026vetime,
  title={VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection},
  author={Yingyuan Yang and Tian Lan and Yifei Gao and Yimeng Lu and Liming An and Wenjun He and Meng Wang and Chenghao Liu and Chen Zhang},
  journal={arXiv preprint arXiv:2602.16681},
  year={2026}
}

References

• TSB-AD: The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark (NeurIPS 2024).
• Time-RCD: Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy (ICML 2026).

License

This project is released under the Apache 2.0 License.