Distilling Multi-Teacher Scoring Principles for Unsupervised Time Series Anomaly Detection

Dongchan Cho; Jiho Han; Juwon Hwang; Changhee Min; Minsang Kim; Namsoon Jung

cs.LGOct 21, 2026

Distilling Multi-Teacher Scoring Principles for Unsupervised Time Series Anomaly Detection

Authors: Dongchan Cho, Jiho Han, Juwon Hwang, Changhee Min, Minsang Kim, Namsoon Jung

Organizations: Industrial AI Lab, SimPlatform Co. Ltd. Affiliate Research Institute

Abstract

Time series anomalies take diverse forms, and individual detectors encode different anomaly scoring principles that rarely cover all anomaly types. Recent unsupervised time-series anomaly detectors provide complementary scoring signals, yet deploying them as an ensemble preserves the inference and maintenance cost of every individual detector. We study whether heterogeneous teacher detectors can be distilled into a single lightweight detector in the unsupervised setting, where no anomaly labels are available to define a shared target. AnoMix addresses this by extracting weak pairwise supervision from the training-set anomaly scores of frozen teachers. It normalizes each teacher score sequence independently and trains the student with CoRank, a multi-teacher ranking-distillation objective that converts teacher score gaps into pairwise supervision through teacher consensus. Across seven benchmark datasets, AnoMix successfully integrates complementary teacher scoring principles into a single deployable model, improving over individual detectors and static score ensembles while using a compact student with under 80k parameters on the benchmark settings. A zero-shot variant further shows that the distilled ranking signal transfers to unseen datasets and can outperform large pretrained time-series foundation models for anomaly detection.

Figures & tables

Figure 1: Training overview of AnoMix. (a) Frozen teachers ( ) score the unlabeled training series, followed by score normalization. (b) CoRank selects informative pairs and derives their consensus direction and confidence. (c) A trainable student ( ) learns the pairwise ordering through a confidence-weighted loss.

Method	PSM		SWaT		Exathlon		SMD		SMAP		Daphnet		SWAN		Avg Rank
Method	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	Avg Rank
Omni [ 3 ]	51.59	22.52	59.42	27.32	99.98	99.75	95.40	69.98	49.93	27.68	78.60	18.38	92.06	91.16	6.18
USAD [ 9 ]	52.15	22.73	68.92	33.39	99.97	99.63	95.98	71.97	49.80	27.58	76.95	17.15	89.80	88.60	6.36
A.T [ 15 ]	53.76	23.13	56.63	23.55	37.70	24.88	44.03	11.80	40.56	22.30	46.28	10.33	86.33	81.07	10.29
TranAD [ 10 ]	55.90	22.25	77.06	37.86	99.50	93.88	86.51	52.05	71.96	37.06	76.71	17.98	92.02	86.48	6.93
TimesNet [ 11 ]	57.91	24.32	59.29	24.35	98.61	85.33	93.96	53.06	59.22	23.90	90.25	33.97	93.87	88.77	6.71

Table 1: Main results on the seven benchmark datasets (5-seed mean, %). Avg Rank is the per-row average rank across the 14 dataset-metric values, where lower is better. Boldface indicates the best result in each column, and underlined values indicate the second-best result.

Method	PSM		SWaT		Exathlon		SMD		SMAP		Daphnet		SWAN		Avg Rank
Method	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	Avg Rank
Mean	67.00	31.23	63.80	27.07	99.65	95.82	97.55	74.23	53.50	34.87	91.64	38.10	93.96	91.54	4.00
Max	66.81	31.20	59.77	25.78	99.45	93.49	96.46	68.26	53.02	35.59	89.35	31.59	93.98	91.30	5.21
Median	61.93	27.28	65.66	28.21	99.83	97.78	96.50	72.13	66.88	32.59	90.24	38.85	92.81	89.47	4.57
Rank Fusion	64.90	27.81	65.08	28.57	99.97	99.74	97.72	83.70	70.26	37.86	92.71	46.11	93.88	91.69	2.93
RRF [ 44 ]	67.37	31.69	65.28	28.55	99.47	94.59	97.73	79.13	71.84	39.62	91.32	36.18	94.24	91.59	3.00

Table 2: Score-ensemble comparison on the seven benchmark datasets (5-seed mean, %). Avg Rank is the per-row average rank across the 14 dataset-metric values, where lower is better. Boldface indicates the best result in each column, and underlined values indicate the second-best result.

Figure 2: Ablation study reporting macro-average V-PR (%) over the seven benchmark datasets. (a) Student backbones: LSTM, Transformer, Dilated-RNN, and TCN. (b) Ranking losses: RankNet, Hinge, ListNet, and Spearman on an averaged teacher-score target, and CoRank. (c) Supervision forms: score regression on the averaged teacher-score target, MeanRank, and CoRank. (d) CoRank decomposition: MeanRank, consensus confidence, consensus support, and full CoRank.

Method	PSM		SWaT		Exathlon		SMD		SMAP		Daphnet		SWAN		Avg Rank
Method	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	Avg Rank
LOO-1	48.10	22.32	43.48	25.09	80.05	81.58	59.08	48.49	56.59	56.58	75.65	30.98	90.81	84.37	7.00
LOO-2	69.91	42.22	73.28	38.72	99.99	99.96	98.92	89.48	98.37	97.65	94.01	51.50	92.45	90.77	4.61
LOO-3	69.04	42.64	76.82	43.37	99.99	99.96	98.87	89.37	98.40	97.72	94.12	51.96	93.75	91.76	3.61
LOO-4	67.55	35.17	80.98	44.59	99.99	99.96	98.90	89.37	98.41	97.75	94.10	51.94	93.60	91.52	3.68
LOO-5	67.64	34.40	75.49	41.28	99.99	99.96	98.90	89.39	98.42	97.77	94.15	52.25	93.43	91.28	3.68

Table 3: Teacher-set variants on the seven benchmark datasets (5-seed mean, %; standard deviations in Appendix O ). LOO-

k

uses the top-

k

teachers from the target-excluded teacher ranking, and Sel-3 is the fixed three-teacher setting used in Table 1 . Avg Rank is computed over the 14 reported values, where lower is better. Boldface indicates the best result in each column, and underlined values indicate the second-best result.

Figure 3: Mean absolute Spearman correlation with teacher score sequences (

|\rho_{S}|

) versus V-PR on five non-saturated datasets. A higher

|\rho_{S}|

indicates stronger alignment with teacher score sequences, while a lower

|\rho_{S}|

indicates weaker alignment. The blue star denotes AnoMix, and the remaining markers denote the score-ensemble baselines (Mean, Max, Median, Rank Fusion, RRF).

Method	Params	PSM		SWaT		Exathlon		SMD		SMAP		Daphnet		SWAN		Avg Rank
Method	Params	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	Avg Rank
MOMENT [ 42 ]	110M	41.87	16.66	54.62	20.17	99.66	97.18	58.22	10.38	56.03	28.31	91.49	34.49	92.06	86.29	5.57
TimesFM [ 45 ]	200M	53.99	20.14	49.45	19.22	99.19	96.64	59.04	16.19	94.65	88.93	89.97	34.79	93.79	87.66	4.50
Chronos [ 46 ]	710M	52.46	18.68	63.11	23.33	87.54	61.15	89.06	34.67	70.54	30.97	90.53	33.50	93.02	88.11	4.71
MOIRAI [ 47 ]	14M	54.02	19.47	55.17	20.49	91.52	66.86	60.99	10.37	57.98	21.12	91.07	34.11	92.64	83.72	5.43
Lag-Llama [ 48 ]	10M	54.63	20.45	47.96	18.56	89.08	58.11	83.10	33.47	80.29	39.28	92.04	38.24	93.11	87.86	4.43

Table 4: Zero-shot transfer results (%). AnoMix-ZS reports five-seed means, with standard deviations in Appendix O ; foundation baselines use fixed released checkpoints. AnoMix-ZS is trained on the other six datasets and evaluated on the held-out target. Boldface indicates the best result in each column, and underlined values indicate the second-best result.

Appendix figures & tables20 assets

Supplementary material from the paper’s appendix.

Appendix

Figure S1 : Test-time anomaly scores. Rows correspond to the seven benchmark datasets and columns to SARAD, CrossAD, OracleAD, and AnoMix. Scores are min-max normalized to $[0,1]$ . Red shaded bands indicate labeled anomaly segments.

Method	PSM			SWaT			Exathlon			SMD			SMAP			Daphnet			SWAN
Method	VUS	AUC	R-AUC	VUS	AUC	R-AUC	VUS	AUC	R-AUC	VUS	AUC	R-AUC	VUS	AUC	R-AUC	VUS	AUC	R-AUC	VUS	AUC	R-AUC
Omni	22.52	21.43	24.07	27.32	24.03	32.12	99.75	98.88	99.96	69.98	71.68	75.18	27.68	26.97	28.08	18.38	16.97	19.25	91.16	54.34	91.51
USAD ’20	22.73	22.52	24.35	33.39	29.60	38.56	99.63	98.47	99.93	71.97	72.42	77.36	27.58	26.86	27.98	17.15	15.24	18.31	88.60	49.50	87.86
A.T	23.13	21.95	23.18	23.55	20.94	26.87	24.88	24.59	25.28	11.80	9.74	13.74	22.30	21.35	22.90	10.33	9.40	11.35	81.07	36.05	85.01
TranAD ’22	22.25	21.27	22.61	37.86	31.38	43.86	93.88	93.35	93.97	52.05	46.92	45.51	37.06	35.94	37.54	17.98	16.48	19.29	86.48	41.25	86.87
TimesNet ’23	24.32	20.33	26.19	24.35	20.87	28.20	85.33	81.74	88.15	53.06	44.41	56.79	23.90	22.80	24.62	33.97	31.01	36.82	88.77	43.48	90.36

Appendix

Table S1 : PR metrics on the seven benchmark datasets (5-seed mean, %). Boldface indicates the best result in each column, and underlined values indicate the second-best result.

Method	PSM			SWaT			Exathlon			SMD			SMAP			Daphnet			SWAN
Method	VUS	AUC	R-AUC	VUS	AUC	R-AUC	VUS	AUC	R-AUC	VUS	AUC	R-AUC	VUS	AUC	R-AUC	VUS	AUC	R-AUC	VUS	AUC	R-AUC
Omni	51.59	59.49	52.74	59.42	59.91	63.64	99.98	99.91	100.00	95.40	95.17	96.34	49.93	48.90	50.83	78.60	79.20	79.01	92.06	63.19	86.54
USAD ’20	52.15	58.18	52.52	68.92	70.88	72.92	99.97	99.88	99.99	95.98	95.40	96.88	49.80	48.77	50.70	76.95	76.13	78.18	89.80	56.15	81.06
A.T	53.76	54.64	44.00	56.63	54.36	59.22	37.70	37.13	38.35	44.03	42.04	44.83	40.56	39.73	41.49	46.28	44.92	47.59	86.33	50.95	78.38
TranAD ’22	55.90	55.14	48.22	77.06	72.97	81.54	99.50	99.47	99.50	86.51	82.75	81.84	71.96	70.85	72.81	76.71	73.04	79.00	92.02	52.60	81.50
TimesNet ’23	57.91	53.22	57.87	59.29	53.56	64.78	98.61	98.24	98.88	93.96	90.60	92.45	59.22	58.06	60.13	90.25	88.98	91.30	93.87	56.52	86.33

Appendix

Table S2 : ROC metrics on the seven benchmark datasets (5-seed mean, %). Boldface indicates the best result in each column, and underlined values indicate the second-best result.

Method	PSM			SWaT			Exathlon			SMD			SMAP			Daphnet			SWAN
Method	F1	Event	Aff	F1	Event	Aff	F1	Event	Aff	F1	Event	Aff	F1	Event	Aff	F1	Event	Aff	F1	Event	Aff
Omni	32.33	33.23	71.17	36.08	42.27	71.63	98.90	100.00	99.92	65.77	73.18	83.23	40.81	100.00	94.05	31.66	33.39	86.13	49.72	49.17	71.05
USAD ’20	31.42	32.11	71.52	45.84	44.86	74.07	98.70	99.85	99.91	64.51	66.15	83.88	40.81	100.00	94.05	30.87	31.11	85.85	49.17	49.17	71.05
A.T	30.13	28.58	66.79	32.80	42.83	70.23	29.57	78.63	87.28	15.07	35.43	64.33	30.92	57.50	77.16	16.68	46.50	79.35	49.17	32.23	53.48
TranAD ’22	29.30	29.20	69.43	48.65	33.39	71.64	97.62	100.00	99.11	52.64	76.91	91.88	63.20	100.00	93.14	29.36	61.49	88.35	49.17	49.17	71.05
TimesNet ’23	29.20	29.20	69.43	30.39	30.26	71.63	90.08	94.00	97.45	51.94	67.34	86.40	40.20	100.00	92.58	40.48	58.10	82.44	49.17	49.17	71.05

Appendix

Table S3 : F1-based metrics on the seven benchmark datasets (5-seed mean, %). Boldface indicates the best result in each column, and underlined values indicate the second-best result.

Dataset	Subset	Domain	$D$	Train	Validation	Test	Anomaly (%)	Events
PSM	–	Server monitoring	25	67,500	7,500	142,624	11.2	72
SWaT	–	Cyber-physical control	66	3,374	375	11,247	13.2	6
Exathlon	id22	Distributed stream processing	31	9,689	1,077	32,300	12.6	3
SMD	id6	Internet-company server	38	6,462	718	21,542	6.1	10
SMAP	id1	Satellite telemetry	25	1,846	206	6,157	13.7	1
Daphnet	–	Wearable accelerometer	9	8,723	970	29,081	5.9	6

Appendix

Table S4 : Statistics of the seven benchmark datasets.

Model	Window	Learning rate	Stopping rule
OmniAnomaly	100	$2\!\times\!10^{-3}$	Early stopping
USAD	100	$10^{-3}$	Early stopping
Anomaly Transformer	50	$10^{-3}$	Early stopping
TranAD	10	$10^{-3}$	Early stopping
TimesNet	96	$10^{-4}$	Early stopping
SARAD	100	$7\!\times\!10^{-4}$	Early stopping

Appendix

Table S5 : Baseline configurations for Table 1 .

Rank	PSM	SWaT	Exathlon	SMD	SMAP	Daphnet	SWAN
1	SARAD	CrossAD	CrossAD	SARAD	CrossAD	CrossAD	SARAD
2	CrossAD	SARAD	SARAD	OracleAD	SARAD	SARAD	CrossAD
3	CATCH	CATCH	CATCH	CrossAD	CATCH	USAD	OracleAD
4	USAD	OracleAD	OracleAD	CATCH	USAD	Omni	CATCH
5	OracleAD	Omni	USAD	TranAD	Omni	OracleAD	USAD
6	Omni	USAD	Omni	USAD	OracleAD	CATCH	Omni

Appendix

Table S6 : LOO teacher ranking on the seven benchmark datasets. For each target dataset, teachers are ranked by average VUS-PR (%) on the other six datasets. Omni denotes OmniAnomaly, and A.T denotes Anomaly Transformer. Boldface indicates the top three teachers for each target dataset.

Teacher set	Mean Spearman	Pair support	AnoMix VUS-PR	vs. best teacher	vs. mean ensemble
Sel-3	0.463	36.8%	$\mathbf{73.93\pm 0.51}$	$+15.49$	$+16.95$
HML-3	0.248	27.1%	$66.17\pm 1.27$	$+9.65$	$+14.11$
Mid-3	0.607	33.9%	$68.94\pm 0.57$	$+10.09$	$+15.15$
Bottom-3	0.024	3.9%	$48.74\pm 3.40$	$-2.17$	$-1.14$
Corr-3	0.844	27.3%	$69.22\pm 0.70$	$+14.67$	$+15.54$

Appendix

Table S7 : Teacher-pool stress test. VUS-PR is reported as mean

\pm

standard deviation (%), and gains are in %pt.

Dataset	Learning Rate	Window Size	Batch Size
PSM	$10^{-3}$	127	64
SWaT	$10^{-3}$	127	64
Exathlon	$10^{-3}$	127	64
SMD	$10^{-5}$	64	64
SMAP	$10^{-3}$	127	64
Daphnet	$10^{-5}$	127	64

Appendix

Table S8 : Per-dataset student hyperparameters.

Figure S2 : Macro-average hyperparameter sensitivity across the seven datasets. The line denotes the macro mean across datasets, and the shaded band denotes the

\pm 1

standard deviation across five random seeds.

Figure S3 : AnoMix CoRank training loss over 10 epochs on each of the six benchmark datasets. The line denotes the five-seed mean, and the shaded region denotes the

\pm 1

standard deviation across five random seeds.

Model	Complexity	Params	Memory (MB)	FLOPs
SARAD	$O(L^{2}D)$	9.66M	58.04	2.03G
CrossAD	$O(L^{2}D)$	2.64M	178.49	8.81G
OracleAD	$O(LD\!+\!D^{2})$	211.72M	893.94	21.21G
CATCH	$O(P^{2}D)$	13.52M	86.17	1.18G
Omni	$O(LD)$	314k	27.55	31.64M
USAD	$O(LD)$	1.00M	13.82	1.60M

Appendix

Table S9 : Model complexity and single-model inference cost on synthetic input (

L{=}100

D{=}200

, batch=1), excluding offline training and teacher scoring.

L

=window,

D

=features,

P

=patch count.

Component	Configuration
CPU	Intel Core Ultra 7 265K, 20 cores
GPU	NVIDIA GeForce RTX 5090, 32 GB memory
System memory	96 GB RAM
Operating system	Ubuntu 24.04.4 LTS, Linux 6.14
NVIDIA driver	570.153.02
Software stack	Python 3.13, PyTorch 2.7.0+cu128, CUDA runtime 12.8

Appendix

Table S10 : System configuration used for the reported experiments.

Figure S4 : Ablation on teacher score normalization (a) and score-gap threshold selection (b). Each bar is the seven-dataset macro VUS-PR averaged over five seeds. Default settings are colored in dark blue.

Dataset	$c\in[0,0.2)$	$c\in[0.2,0.4)$	$c\in[0.4,0.6)$	$c\in[0.6,0.8)$	$c\in[0.8,1]$
PSM	0.04	0.09	0.08	0.40	99.39
SWaT	4.06	5.16	7.36	11.65	71.76
Exathlon	0.01	0.02	0.02	0.05	99.91
SMD	3.72	4.52	6.14	9.85	75.77
SMAP	2.30	2.02	2.71	5.61	87.36
Daphnet	0.40	0.58	0.83	2.22	95.96

Appendix

Table S11 : Confidence distribution among retained training pairs (%). Rows sum to

100\%

up to rounding.

Training confidence range	VUS-ROC	VUS-PR
$[0,1]$ (Full)	$78.55\pm 4.32$	$42.39\pm 3.34$
$[0.2,1]$	$75.93\pm 5.74$	$39.74\pm 4.25$
$[0.4,1]$	$75.76\pm 5.88$	$41.39\pm 4.94$
$[0.6,1]$	$73.84\pm 6.10$	$39.56\pm 3.82$
$[0.8,1]$	$73.12\pm 7.32$	$40.79\pm 5.56$

Appendix

Table S12 : Confidence-filtered training on SWaT (5-seed mean

\pm

standard deviation, %).

Method	PSM		SWaT		Exathlon		SMD		SMAP		Daphnet		SWAN
Method	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR
Omni [ 3 ]	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
USAD [ 9 ]	0.89	0.57	1.31	0.75	0.00	0.04	0.34	1.31	0.00	0.00	0.84	0.38	0.20	0.29
A.T [ 15 ]	4.04	2.59	10.16	6.40	6.57	6.97	2.63	1.03	7.06	4.02	7.63	1.77	3.05	0.96
TranAD [ 10 ]	0.59	0.19	1.25	0.78	0.01	0.36	0.15	0.17	11.31	3.95	0.34	0.25	0.18	0.28
TimesNet [ 11 ]	0.69	0.58	1.01	0.89	0.11	0.84	0.65	2.05	4.50	1.85	0.15	0.59	0.46	0.90

Appendix

Table S14 : Standard deviation across five random seeds (%) for the main results in Table 1 .

Method	PSM		SWaT		Exathlon		SMD		SMAP		Daphnet		SWAN
Method	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR
Mean	2.66	1.59	3.54	1.29	0.13	1.76	0.28	1.24	0.58	0.14	0.88	3.92	0.32	0.30
Max	3.91	2.46	6.60	2.80	0.18	2.74	0.49	1.42	0.41	0.08	1.46	3.67	0.52	0.14
Median	2.01	0.86	0.68	0.31	0.21	2.49	0.34	1.38	0.82	0.41	0.38	1.38	0.89	1.85
Rank Fusion	1.28	0.86	2.47	1.14	0.01	0.08	0.50	2.13	0.93	1.04	0.44	2.14	0.19	0.34
RRF [ 44 ]	1.33	1.45	2.48	1.30	0.12	1.17	0.15	1.32	1.55	1.51	0.77	2.82	0.37	0.91

Appendix

Table S15 : Standard deviation across five random seeds (%) for the score-ensemble comparison in Table 2 .

Method	PSM		SWaT		Exathlon		SMD		SMAP		Daphnet		SWAN
Method	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR
LOO-1	6.07	4.27	22.60	10.70	39.37	36.16	34.56	33.08	39.45	36.99	29.93	13.42	3.05	5.10
LOO-2	2.41	3.93	2.14	2.02	0.01	0.03	0.42	0.76	0.06	0.06	0.28	1.76	0.18	0.33
LOO-3	6.25	5.11	3.10	4.75	0.01	0.04	0.42	1.29	0.04	0.05	0.38	2.01	0.51	0.67
LOO-4	3.11	2.55	3.16	3.35	0.00	0.02	0.38	0.99	0.04	0.04	0.38	1.99	0.43	0.80
LOO-5	3.86	2.53	7.15	5.06	0.01	0.04	0.38	0.99	0.04	0.04	0.38	2.01	0.27	0.84

Appendix

Table S16 : Per-dataset standard deviations (%pt) for the teacher sets in Table 3 .

Method	PSM		SWaT		Exathlon		SMD		SMAP		Daphnet		SWAN
Method	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR	V-ROC	V-PR
AnoMix-ZS	1.45	1.45	1.46	2.17	0.08	1.38	0.47	4.72	0.75	2.30	1.00	4.63	0.25	0.53

Appendix

Table S17 : Per-dataset standard deviations (%pt) for AnoMix-ZS in Table 4 .

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Oct 12, 2026

OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection

Time series (TS) anomaly detection (AD) lacks a universal detector, with the best choice determined by the series rather than by the algorithm. Existing methods therefore either select one detector for each series or average a pool of them. However, both assume that no label ever arrives, while a deployed monitor does receive them, since an operator confirms the alarms it raises. In this paper, we propose ONESHOT, which uses a single confirmed anomaly to weight the pool rather than to choose one detector from it. Specifically, we score each detector on the confirmed anomaly, and those scores become the weights of a soft mixture over the whole pool. We show that a single confirmed anomaly is enough to come close to the best detector for the series. Across 152 univariate and 73 multivariate series, ONESHOT outperforms the strongest single detector, averaging the whole pool, and even picking the best detector on the confirmed event. Code is available at: https://github.com/seunghan96/one_shot.

Seunghan Lee, Jun Seo, Jaehoon Lee +9

LG AI Research

Time-Series Anomaly Detection Anomaly Detection Few-Shot Anomaly Detection

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Jun 18, 2026

PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection

Time-series anomaly detection has significant practical value for industrial and medical monitoring, as well as other critical domains. Current Transformer- and large-model-based detection approaches incur excessive computational overhead, while existing lightweight alternatives are constrained by insufficient feature extraction and inadequate modeling of dependencies across multivariate variables. To mitigate the above drawbacks, this study develops a lightweight, efficient anomaly detection model, dubbed PaAno, within the patch-oriented representation learning paradigm. In the encoder module, a multiscale feature-extraction backbone is constructed using convolutional kernels with differentiated receptive fields to capture hierarchical temporal characteristics; subsequent cross-scale adaptive attention aggregation, combined with residual connection optimization, further stabilizes feature representation learning. A cross-variable fusion attention module is embedded to explicitly characterize inter-variable correlations, empowering the model to identify anomalous patterns amid intricate operational conditions. Moreover, a novel pretext task based on temporal patch-window sorting is customized to uncover intrinsic structural properties of time series, and triplet loss is leveraged to optimize the patch embedding space for enhanced feature discrimination. Extensive experiments on the TSB-AD benchmark demonstrate that the proposed PaAno achieves state-of-the-art detection accuracy on both univariate and multivariate tasks, yielding significant performance gains across evaluation metrics, including VUS-PR, relative to the original PaAno. Leveraging a compact network design, the presented model achieves favorable computational efficiency, enabling deployment on resource-limited terminals for real-time anomaly inference.

Youji Zhu, Hongbing Wang, Wenchao Liu +2

School of Mathematical Sciences, Guizhou Normal University, Guiyang 550025, China. · School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550025, China.

Self-Supervised Time Series Representation Learning Time-Series Anomaly Detection Multiscale Time-Series Modeling

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Feb 1, 2026

PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection

Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios. Moreover, they often fail to demonstrate significant performance gains over simpler methods under rigorous evaluation protocols. In this study, we propose Patch-based representation learning for time-series Anomaly detection (PaAno), a lightweight yet effective method for fast and efficient time-series anomaly detection. PaAno extracts short temporal patches from time-series training data and uses a 1D convolutional neural network to embed each patch into a vector representation. The model is trained using a combination of triplet loss and pretext loss to ensure the embeddings capture informative temporal patterns from input patches. During inference, the anomaly score at each time step is computed by comparing the embeddings of its surrounding patches to those of normal patches extracted from the training time-series. Evaluated on the TSB-AD benchmark, PaAno achieved state-of-the-art performance, significantly outperforming existing methods, including those based on heavy architectures, on both univariate and multivariate time-series anomaly detection across various range-wise and point-wise performance measures.

Jinju Park, Seokho Kang

Department of Industrial Engineering, Sungkyunkwan University

Self-Supervised Time Series Representation Learning Time Series Representation Learning Time-Series Anomaly Detection

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Abstract

Unsupervised Anomaly Detection

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OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection

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