VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection

Yingyuan Yang; Tian Lan; Yifei Gao; Yimeng Lu; Liming An; Wenjun He; Meng Wang; Chenghao Liu

cs.CVFeb 18, 2026

VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection

Authors: Yingyuan Yang, Tian Lan, Yifei Gao, Yimeng Lu, Liming An, Wenjun He, Meng Wang, Chenghao Liu,

Organizations: Department of Industrial Engineering, Tsinghua University, Beijing, China · Lab, Huawei Technologies Ltd, Beijing, China · Datadog AI Research.

Abstract

Time-series anomaly detection (TSAD) requires identifying both immediate Point Anomalies and long-range Context Anomalies. However, existing foundation models face a fundamental trade-off: 1D temporal models provide fine-grained pointwise localization but lack a global contextual perspective, while 2D vision-based models capture global patterns but suffer from information bottlenecks due to a lack of temporal alignment and coarse-grained pointwise detection. To resolve this dilemma, we propose VETime, the first TSAD framework that unifies temporal and visual modalities through fine-grained visual-temporal alignment and dynamic fusion. VETime introduces a Reversible Image Conversion and a Patch-Level Temporal Alignment module to establish a shared visual-temporal timeline, preserving discriminative details while maintaining temporal sensitivity. Furthermore, we design an Anomaly Window Contrastive Learning mechanism and a Task-Adaptive Multi-Modal Fusion to adaptively integrate the complementary perceptual strengths of both modalities. Extensive experiments demonstrate that VETime significantly outperforms state-of-the-art models in zero-shot scenarios, achieving superior localization precision with lower computational overhead than current vision-based approaches. Code available at: https://github.com/yyyangcoder/VETime .

Figures & tables

Figure 1: Comparison of the previous TSAD methods and the proposed VETime. While existing methods often rely on single-modality or coarse visual mapping, VETime introduces fine-grained temporal alignment and dynamic fusion to bridge the gap between visual and temporal perceptions.

Figure 2: Overview of the proposed framework. The time series is first processed by a time-series encoder to extract temporal features

F_{TS}

while simultaneously undergoing Traceable Image Conversion to generate visual input. Then visual features

F_{V_{0}}

extracted from a frozen pre-trained image encoder are subsequently transformed into

F_{V}

through Patch-level Temporal Alignment to reinforce their temporal positional association. Then,

F_{TS}

and

F_{V}

are input into the Anomaly Window Contrastive Learning to derive an anomaly-enhanced representation

F_{A}

. Finally, a Task-adaptive Multi-modal Fusion module integrates all features (

F_{A}

F_{TS}

F_{V}

). Final outputs

F_{AD}

and

F_{Rec}

are mapped to the original sequence length via token projection for the respective anomaly classification and reconstruction heads.

Figure 3: The framework of Traceable Image Conversion and Patch-level Temporal Alignment.

Figure 4: An illustration of Anomaly window contrastive learning

Metric	Model	Univariate Datasets											Total	Total	Avg
		IOPS	MGAB	NAB	NEK	Power	SED	Stock	TODS	UCR	WSD	YAHOO	1st	2nd	Rank
Affiliation-F1	VETime	90.53	68.03	88.57	79.56	78.78	97.31	69.60	85.85	85.06	94.31	97.15	04	04	2.55
	TimeRCD	83.28	70.69	82.48	79.73	85.51	96.87	71.84	86.37	84.63	90.33	96.65	02	03	3.27
	DADA $\dagger$	89.37 *	67.66 *	86.56	95.40	69.79	65.18	98.77	76.89	72.21	93.92	92.20 *	02	00	4.00
	TS-Pulse	68.76	67.33	70.80	73.05	69.94	67.44	67.93	67.90	67.70	68.22	70.05	00	00	6.64
	MOMENT $\dagger$	87.54 *	66.76 *	90.45 *	92.26	75.97	59.13	45.26	59.76	75.77	95.39	79.99 *	02	00	4.73

Table 1: Performance of VETime against zero-shot baselines on 26 public datasets (11 univariate and 15 multivariate). Asterisked (*) results are excluded from ranking due to data leakage of models marked with (

\dagger

). Red : best, Blue : second best.

Metric	Method	NAB	YAHOO	SMAP	MSL
Affiliation -F1	VETime	88.57	97.15	98.83	92.78
	VIT4TS	61.12	60.66	91.55	65.80
	VLM4TS	70.37	64.25	94.76	66.15
	VisualTimeAnomaly	62.46	61.03	91.65	86.20
	AnomLLM	58.53	36.43	42.52	46.08
F1_T	VETime	44.31	91.54	70.21	50.38

Table 2: Performance of VETime against vision-based baselines (SMAP and MSL are univariate settings following VLM4TS ( He et al., 2026 ) ).

Table 7

Figure 5: Effective Receptive Field (ERF) analysis comparing 1D temporal and 2D visual branches across different anomaly types. ERFs are computed via tracking the gradient flow from the final anomaly output back to the raw 1D or 2D inputs, with (b) and (d) averaged across the entire targeted region. 1D temporal branch (a, b) is strictly confined to local contexts, whereas 2D visual branch (c, d) exhibits period-aligned attention peaks that capture cross-period global awareness.

Figure 8: Qualitative anomaly detection results. The top row displays the time series with ground-truth anomalies (red shading), followed by anomaly scores from TimeRCD, VLM4TS, and VETime.

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Name	Domain	#TS	Avg Length	AR (%)
UCR	Misc.	228	67818.7	0.6
NAB	Web	28	5099.7	10.6
YAHOO	Web	259	1560.2	0.6
IOPS	Operations	17	72792.3	1.3
MGAB	Sensor	9	97777.8	0.2
SED	Energy	3	23332.3	4.1

Appendix

Table 5: Univariate Datasets

Name	Domain	#TS	Avg Length	AR (%)
MSL	Space	16	3119.4	5.1
PSM	Sensor	1	217624.0	11.2
SMAP	Space	27	7855.9	2.9
SMD	Server	22	25466.4	3.8
SWaT	ICS	2	207457.5	12.7
GHL	Reservoir	25	199001.0	1.1

Appendix

Table 6: Multivariate Datasets

Metric	Model	Univariate Datasets											Total	Total	Avg
		IOPS	MGAB	NAB	NEK	Power	SED	Stock	TODS	UCR	WSD	YAHOO	1st	2nd	Rank
Full-Shot Models
Affiliation-F1	VETime	90.53	68.03	88.57	79.56	78.78	97.31	69.60	85.85	85.06	94.31	97.15	07	00	1.91
	TranAD	83.19	67.28	90.28	85.02	71.56	61.03	57.94	52.76	73.31	84.34	76.08	00	04	3.45
	USAD	71.08	67.81	91.54	71.13	76.48	55.60	35.92	47.90	76.00	65.10	53.05	00	02	4.36
	OmniAnomaly	80.32	67.35	92.35	86.30	78.16	61.26	75.24	50.73	73.53	78.02	71.31	03	01	3.00

Appendix

Table 7: Performance of VETime (zero-shot) against full-shot baselines on 11 public univariate datasets. Red : best, Blue : second best.

Metric	Model	Multivariate Datasets					Additional Datasets										Total	Total
Metric	Model	MSL	PSM	SMAP	SMD	SWaT	GHL	Daph.	Exath.	Gen.	GECCO	MIT	SVDB	LTDB	CATS	TAO	1st	2nd
Full-Shot Models
Affiliation-F1	VETime	87.02	77.27	87.06	88.94	75.45	74.93	84.61	99.90	75.41	87.63	75.16	73.03	78.28	72.08	75.41	12	01
	TranAD	79.91	73.83	87.39	92.20	75.37	39.00	72.00	94.00	69.00	26.00	71.00	71.00	64.00	75.00	41.00	02	09
	USAD	81.86	57.86	87.25	85.09	75.06	35.95	19.82	84.96	46.84	14.51	63.28	58.33	35.81	57.04	2.05	00	00
	OmniAnomaly	83.15	58.17	91.38	85.82	73.39	40.90	67.74	93.89	68.63	18.28	70.95	71.96	64.86	74.32	20.31	01	05

Appendix

Table 8: Performance of VETime (zero-shot) against full-shot baselines on multivariate datasets. Red : best, Blue : second best.

Metric

Method

IOPS

MGAB

NAB

NEK

Power

SED

Stock

TODS

UCR

WSD

YAHOO

Affiliation-F1

TimeRCD

83.58

\pm

0.62

70.19

\pm

1.39

82.17

\pm

1.99

78.56

\pm

2.20

85.25

\pm

1.71

96.90

\pm

0.42

71.66

\pm

1.05

86.66

\pm

2.60

84.78

\pm

0.40

90.12

\pm

0.23

96.54

\pm

0.37

DADA

73.00

\pm

0.92

66.28

\pm

1.66

84.51

\pm

0.63

78.20

\pm

1.81

68.05

\pm

0.87

68.20

\pm

1.58

96.55

\pm

0.49

66.31

\pm

0.32

66.36

\pm

0.23

86.10

\pm

0.73

90.46

\pm

0.64

VETime

90.22

\pm

0.66

68.18

\pm

0.18

88.19

\pm

0.43

79.95

\pm

0.97

78.99

\pm

0.33

97.95

\pm

0.98

69.45

\pm

0.61

85.43

\pm

0.54

85.06

\pm

0.24

94.28

\pm

0.06

97.12

\pm

0.16

VUS-PR

TimeRCD

21.82

\pm

3.79

1.03

\pm

0.06

24.42

\pm

1.35

27.47

\pm

2.65

21.17

\pm

0.15

80.38

\pm

0.90

76.72

\pm

1.45

93.29

\pm

1.69

23.08

\pm

0.07

21.94

\pm

2.71

84.23

\pm

0.75

DADA

24.77

\pm

1.85

0.62

\pm

0.04

24.46

\pm

0.79

64.82

\pm

2.41

12.42

\pm

0.64

6.43

\pm

0.26

99.79

\pm

0.02

57.47

\pm

0.11

2.12

\pm

0.65

30.38

\pm

1.05

73.05

\pm

0.47

VETime

30.84

\pm

1.62

0.69

\pm

0.02

32.84

\pm

0.21

56.42

\pm

2.71

11.85

\pm

0.18

88.37

\pm

2.24

76.21

\pm

0.78

93.82

\pm

0.53

24.67

\pm

0.78

33.16

\pm

0.65

87.92

\pm

1.06

Appendix

Table 9: Performance comparison of supervised baselines re-trained on the same synthetic dataset (mean

\pm

standard deviation over 5 runs). Best result is in red , second-best is in blue .

Metric

Method

IOPS

MGAB

NAB

NEK

Power

SED

Stock

TODS

UCR

WSD

YAHOO

Zero-Shot Models

Affiliation-F1

VETime

90.39

\pm

3.10

68.17

\pm

0.61

88.36

\pm

3.38

74.96

\pm

17.28

78.78

\pm

0.00

97.08

\pm

1.34

69.63

\pm

0.54

85.84

\pm

5.26

85.08

\pm

1.54

93.25

\pm

1.48

97.07

\pm

1.79

TimeRCD

83.37

\pm

3.67

70.71

\pm

1.85

83.07

\pm

9.54

82.39

\pm

12.56

85.51

\pm

0.00

96.48

\pm

1.79

71.77

\pm

1.49

86.58

\pm

8.30

84.68

\pm

1.86

90.36

\pm

2.70

96.75

\pm

1.29

DADA

89.61

\pm

1.51

67.70

\pm

0.40

87.51

\pm

6.79

95.79

\pm

0.13

69.79

\pm

0.00

65.18

\pm

2.03

98.83

\pm

0.17

76.91

\pm

12.69

72.20

\pm

0.83

93.81

\pm

2.72

92.33

\pm

3.51

MOMENT

87.60

\pm

2.02

67.08

\pm

3.42

91.18

\pm

4.32

91.53

\pm

5.67

75.97

\pm

0.00

58.19

\pm

4.52

45.28

\pm

0.91

59.76

\pm

13.08

75.61

\pm

2.55

95.46

\pm

1.86

79.81

\pm

3.79

TimesFM

81.93

\pm

3.85

66.84

\pm

0.95

79.63

\pm

3.86

95.17

\pm

1.76

69.88

\pm

0.00

66.18

\pm

3.40

97.51

\pm

0.42

88.91

\pm

4.93

69.97

\pm

1.22

78.97

\pm

1.75

91.34

\pm

1.81

Appendix

Table 10: Performance of VETime against zero-shot and full-shot time-series baselines across 11 datasets (Mean

\pm

95% CI). Best result is in red , second-best is in blue .

Metric	Method	NAB	YAHOO	SMAP	MSL
Affiliation-F1	VETime	88.36 $\pm$ 3.38	97.07 $\pm$ 1.79	95.13 $\pm$ 3.72	90.66 $\pm$ 2.65
	VIT4TS	64.64 $\pm$ 11.91	65.48 $\pm$ 3.67	93.55 $\pm$ 4.20	69.90 $\pm$ 22.83
	VLM4TS	60.50 $\pm$ 13.28	64.27 $\pm$ 3.66	94.48 $\pm$ 3.42	64.63 $\pm$ 25.15
	VisualTimeAnomaly	62.12 $\pm$ 4.25	60.80 $\pm$ 3.10	90.89 $\pm$ 3.76	86.23 $\pm$ 3.33
	AnomLLM	58.23 $\pm$ 10.61	36.36 $\pm$ 4.24	42.03 $\pm$ 20.36	46.34 $\pm$ 20.64
$F1_{T}$	VETime	44.22 $\pm$ 6.53	91.54 $\pm$ 1.65	66.04 $\pm$ 4.28	49.07 $\pm$ 6.93

Appendix

Table 11: Performance of VETime against vision-based baselines across 4 datasets (Mean

\pm

95% CI). Best result is in red , second-best is in blue .

Figure 9: Performance of VETime (strictly zero-shot) against zero-shot and full-shot models. Metrics: Average Rank (lower is better) across 4 metrics on different datasets.

Figure 10: Quantitative validation in feature separability between normal and anomalous representations. The clustering metrics clearly illustrate that the 2D visual transformation introduces global awareness, while the final fusion

F_{AD}

maximizes the overall normal/anomalous separability.

Model	Params. (M)	Mem. (MB)	Avg. Time (ms)	NAB	WSD	YAHOO
Base	37.06	223.73	24.49	82.48 / 24.32	90.33 / 21.77	96.65 / 84.41
Dual-TS	160.49	928.52	48.19	80.84 / 30.28	88.87 / 27.24	96.81 / 87.25
VETime	157.96	787.41	51.02	88.57 / 32.98	94.31 / 33.85	97.15 / 88.61

Appendix

Table 12: Efficiency and performance comparison. Metrics are reported as Affiliation-F1 / VUS-PR (%). Best result is in red, second-best is in blue.

Model	Params. (M)	NAB	WSD	YAHOO
Resnet	25.63	78.41 / 22.96	75.17 / 13.57	70.85 / 15.75
ConvNeXt-Base	88.58	78.51 / 23.36	82.15 / 25.38	79.73 / 38.90
CLIP-Base	85.64	88.42 / 32.72	93.01 / 30.90	95.87 / 86.12
MAE-Base	85.64	88.57 / 32.98	94.31 / 33.85	97.15 / 88.61
MAE-Large	303.10	88.67 / 31.54	93.94 / 34.10	97.03 / 87.20

Appendix

Table 13: Ablation study and comparison of vision encoders (Affiliation-F1 / VUS-PR (%)). Best result is in red, second-best is in blue.

Figure 11: Qualitative comparison of different imaging strategies.

Imaging Strategies	Point	Contextual	Mixed
Base	85.65 / 67.31	77.18 / 23.89	83.90 / 39.35
(1) GAF	86.12 / 72.88	77.01 / 23.57	84.63 / 42.05
(2) RP	85.79 / 73.91	77.07 / 23.87	84.88 / 42.65
(3) Line plot	86.14 / 74.82	78.20 / 24.92	84.77 / 45.31
(4) Gray mappings	87.97 / 77.74	79.50 / 26.65	86.07 / 46.48
(5) Shuffled RGB mappings	88.75 / 78.42	78.94 / 25.51	84.43 / 46.36

Appendix

Table 14: Performance comparison under different imaging strategies (Affiliation-F1 / VUS-PR (%)). The time series from 11 univariate datasets are categorized as: Point (short-duration spikes), Contextual (long-duration structural shifts), and Mixed . Base : TS Encoder + Classifier. Shuffled RGB mappings : Temporal order shuffled before mapping and restored before alignment. Best result is in bold red , second-best is in bold blue .

Strategy	IOPS	MGAB	NAB	NEK	Power	SED	Stock	TODS	UCR	WSD	YAHOO
Direct feature-level fusion	87.40 / 22.67	67.51 / 0.73	83.31 /26.99	79.07/50.03	83.62/21.34	95.58/ 79.05	68.18/ 74.38	86.30 / 93.14	84.06/ 22.48	89.53 / 22.76	94.97 /80.33
Score-level fusion	86.78/20.35	67.09/0.71	81.91/ 27.04	81.09 / 60.98	83.96 / 21.36	93.87/77.53	68.33 /74.33	86.04/92.04	84.33 /22.32	89.51/22.43	94.22/ 82.34
Decision-level fusion	86.24/16.07	67.93 /0.70	80.89/25.50	79.50/48.66	83.04/21.16	95.79 /77.32	66.93/71.92	86.12/92.57	82.27/18.38	89.44/18.20	93.47/79.43
Alignment feature-level fusion	88.40 / 25.75	67.36/ 0.75	82.93 / 27.75	79.54 / 50.63	85.63 / 21.41	96.36 / 84.84	68.65 / 75.84	86.53 / 93.55	84.88 / 23.37	90.56 / 24.70	95.19 / 83.82

Appendix

Table 15: Performance comparison of different multimodal fusion strategies (Affiliation-F1 / VUS-PR (%)). Best result is in red , second-best is in blue .

Variant	$\mathcal{L}_{MSE}$	$\mathcal{L}_{aw}$	$\mathcal{L}_{e}$	NAB	WSD	YAHOO
Base				82.93 / 27.75	90.56 / 24.70	95.19 / 83.82
Only $\mathcal{L}_{MSE}$	$\checkmark$			82.05 / 27.64	90.10 / 24.86	95.20 / 83.29
Only $\mathcal{L}_{aw}$		$\checkmark$		83.99 / 29.52	90.11 / 26.30	96.86 / 85.31
w/o $\mathcal{L}_{e}$	$\checkmark$	$\checkmark$		84.62 / 28.81	90.72 / 27.47	96.64 / 85.56
w/o $\mathcal{L}_{MSE}$		$\checkmark$	$\checkmark$	86.96 / 28.44	90.87 / 28.63	96.88 / 86.58
w/o $\mathcal{L}_{aw}$	$\checkmark$		$\checkmark$	86.84 / 31.23	91.63 / 29.00	97.11 / 87.26

Appendix

Table 16: Ablation study on training objectives (Affiliation-F1 / VUS-PR (%)). Base:

\mathcal{L}_{BCE}

. Best result is in red, second-best is in blue.

Variant

TIC

PTA

AWCL

TMF

IOPS

MGAB

NAB

NEK

Power

SED

Stock

TODS

UCR

WSD

YAHOO

Base

83.28/20.23

70.69 / 1.05

82.48/24.32

79.73 /27.88

85.51/21.25

96.87/80.75

71.84 / 77.28

86.37/93.46

84.63/23.09

90.33/21.77

96.65/84.41

✓

87.40/22.67

67.51/0.73

83.31/26.99

79.07/50.03

83.62/21.34

95.58/79.05

68.18/74.38

86.30/93.14

84.06/22.48

89.53/22.76

94.97/80.33

✓

88.40/25.75

67.36/0.75

82.93/27.75

79.54/50.63

85.63 / 21.41

96.36/84.84

68.65/75.84

86.53 /93.55

84.88 /23.37

90.56/24.70

95.19/83.82

✓

90.53/26.15

68.23/0.77

83.99/29.52

79.74 /53.10

85.78 / 21.48

97.16 /86.94

70.89 / 76.58

86.73 /93.76

85.06 /23.99

90.11/26.30

96.86/85.31

✓

90.84 / 28.15

68.73 / 0.78

86.84 / 31.23

79.32/ 55.38

79.08/11.54

96.79/ 87.39

69.07/75.68

85.60/ 93.90

84.46/ 24.83

91.63 / 29.00

97.11 / 87.26

✓

90.56/26.26

67.22/0.75

83.23/24.81

78.85/48.04

78.63/11.33

95.78/85.37

69.60/75.60

86.05/93.20

84.75/24.40

90.54/24.91

96.06/82.62

Appendix

Table 17: Ablation on all 11 datasets (Affiliation-F1 / VUS-PR (%)). Best results are in red, and second-best are in blue. Base: TS Encoder + Classifier, TIC: Traceable Image Conversion, PTA: Patch-level Temporal Alignment, AWCL: Anomaly Window Contrastive Learning, TMF: Task-adaptive Multi-modal Fusion.

Model	Component	NAB	WSD	YAHOO
PTA	w/o adaptive folding	87.81 / 29.99	91.49 / 30.41	97.10 / 86.70
	w/o positional encoding	88.16 / 30.27	92.29 / 31.18	97.25 / 86.81
	w/o attention	85.69 / 29.88	91.57 / 29.46	96.92 / 85.53
AWCL	w/o inter-window contrast	86.69 / 31.17	92.85 / 30.54	97.15 / 87.29
	w/o intra-window contrast	86.15 / 30.86	92.93 / 30.57	97.77 / 88.29
TMF	Concatenation	85.07 / 28.54	89.80 / 26.39	95.69 / 85.72

Appendix

Table 18: Ablation study grouped by model components: PTA (Patch-level Temporal Alignment), AWCL (Anomaly Window Contrastive Learning), and TMF (Task-adaptive Multi-modal Fusion). Metrics are reported as Affiliation-F1 / VUS-PR (%). Best result is in red, second-best is in blue.

Figure 12: Initial time series and mapping images.

Figure 13: Qualitative visualization of anomaly detection results. Blue curve: original time series; red markers: ground-truth anomaly labels; green curve: predicted anomaly scores.

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Masked Autoencoders Vision Foundation Model Adaptation Reconstruction-Based Anomaly Detection Time-Series Anomaly Detection

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Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems. Existing methods require training one specific model for each dataset, which exhibits limited generalization capability across different target datasets, hindering anomaly detection performance in various scenarios with scarce training data. To address this limitation, foundation models have emerged as a promising direction. However, existing approaches either repurpose large language models (LLMs) or construct largescale time series datasets to develop general anomaly detection foundation models, and still face challenges caused by severe cross-modal gaps or in-domain heterogeneity. In this paper, we investigate the applicability of large-scale vision models to TSAD. Specifically, we adapt a visual Masked Autoencoder (MAE) pretrained on ImageNet to the TSAD task. However, directly transferring MAE to TSAD introduces two key challenges: overgeneralization and limited local perception. To address these challenges, we propose VAN-AD, a novel MAE-based framework for TSAD. To alleviate the over-generalization issue, we design an Adaptive Distribution Mapping Module (ADMM), which maps the reconstruction results before and after MAE into a unified statistical space to amplify discrepancies caused by abnormal patterns. To overcome the limitation of local perception, we further develop a Normalizing Flow Module (NFM), which combines MAE with normalizing flow to estimate the probability density of the current window under the global distribution. Extensive experiments on nine real-world datasets demonstrate that VAN-AD consistently outperforms existing state-of-the-art methods across multiple evaluation metrics.We make our code and datasets available at https://github.com/PenyChen/VAN-AD.

Abstract

Time-Series Anomaly Detection

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