LEARN-TS: LLM-Enhanced Alignment and Reconstruction with Normality Guidance for Multivariate Time-Series Anomaly Detection

Jahyeob Koo; Kio Yun; Byoungmo Koo; Jun-Geol Baek

cs.LGSep 30, 2026

LEARN-TS: LLM-Enhanced Alignment and Reconstruction with Normality Guidance for Multivariate Time-Series Anomaly Detection

Authors: Jahyeob Koo, Kio Yun, Byoungmo Koo, Jun-Geol Baek

Organizations: Department of Industrial and Management Engineering, Korea University Seoul, Republic of Korea

Abstract

Reconstruction errors in multivariate time-series anomaly detection may not reliably distinguish abnormal behavior from benign deviations. Language-derived semantics offer complementary context, but existing multimodal approaches may rely on time-associated paired textual information that is difficult to obtain consistently and is not provided by standard multivariate time-series anomaly detection benchmarks. This setting poses two challenges: (1) conditioning masked reconstruction on window-specific semantics without exposing exact numerical targets or anomaly-specific cues, and (2) using a window-independent concept of normality as a complementary semantic reference rather than an independent anomaly detector. We propose LLM-Enhanced Alignment and Reconstruction with Normality Guidance for Time Series (LEARN-TS), which uses a frozen language model to construct two role-separated semantic representations without requiring temporally paired external text. Window-specific observation semantics encode temporal and cross-variable context without exact numerical values to guide channel-shared patch-masked reconstruction. A fixed, dataset-agnostic normality prompt provides a window-independent semantic reference for aligning normal representations and estimating normality discrepancy. At inference, masking each temporal patch once yields timestamp-level reconstruction evidence, conditionally modulated by discrepancy from a separate unmasked view. Across four benchmarks, LEARN-TS achieves the highest mean performance in 13 of 16 dataset-metric comparisons. Controlled ablations examine observation conditioning, joint normality alignment and scoring, and reference content, showing dataset-dependent ranking benefits and modest average gains from semantic over random references.

Figures & tables

Figure 1: (a) Time-associated paired external textual context may vary across datasets and monitored intervals, providing context-dependent semantic information. (b) LEARN-TS instead derives a shared, window-independent normality reference from a fixed, dataset-agnostic prompt applied unchanged across all datasets and windows.

Figure 2: Overview of the three-phase LEARN-TS framework.

Dataset	Metric	Omni	A.T.	Times	Patch	iTrans	G4TS	TLLM	LMixer	CALF	DADA	LEARN-TS
SWaT	A-PR	0.1492	0.6829	0.1341	0.0890	0.0931	0.0894	0.0846	0.0866	0.0819	0.5385	0.7578
	V-PR	0.1371	0.4508	0.1656	0.0982	0.1008	0.0991	0.0930	0.0951	0.0907	0.4543	0.4859
	R-F1	0.1718	0.1602	0.1593	0.1097	0.1277	0.1344	0.1190	0.1201	0.1137	0.0990	0.3102
	Aff-F1	0.7137	0.7413	0.7940	0.6376	0.6621	0.6826	0.6658	0.6737	0.6570	0.7119	0.7277
SMD	A-PR	0.3713	0.2575	0.4640	0.4893	0.4726	0.4947	0.4514	0.4307	0.4578	0.4754	0.4930
	V-PR	0.3752	0.3214	0.6184	0.6376	0.6250	0.6455	0.5839	0.5662	0.5910	0.5912	0.5398

Table 1: Performance comparison on four real-world multivariate time-series anomaly detection datasets. Results are averaged over five runs; standard deviations are reported in Table 7 . For each dataset–metric pair, the best and second-best results are shown in bold and underlined , respectively.

Figure 3: Effect of observation–window correspondence: (a)–(b) matched vs. shuffled observations on SWaT and MSL; (c) A-PR and V-PR gains across all four datasets.

Figure 4: Event-level predictions on three SMD entities: Machine 1-3 with intermediate event density, Machine 2-3 with sparse events, and Machine 2-9 with dense events.

Figure 5: Sensitivity to (a) $\lambda_{\mathrm{full}}$ , (b) $\lambda_{\mathrm{norm}}$ , (c) $\lambda_{\mathrm{gate}}$ , and (d) the number of masked training patches. Results are four-dataset macro averages using a fixed seed.

Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

Dataset	Domain	Entities	Variables	Official train	Official test	Anomaly (%)
SWaT	Industrial control	1	51	496,800	449,919	11.98
SMD	Server monitoring	28	38	708,405	708,420	4.16
PSM	Application server	1	25	132,481	87,841	27.75
MSL	Spacecraft telemetry	27	55	58,317	73,729	10.72

Appendix

Table 3: Dataset statistics before the internal training–validation split. For SMD and MSL, sequence lengths are summed over all entities.

Hyperparameter	Value
Input window length $L$	128
Patch length $\ell$	16
Number of patches $P$	8
Hidden dimension $d$	768
Time-series encoder layers	3
Cross-modal fusion layers	2

Appendix

Table 4: Common model and optimization settings used across all datasets.

Dataset	$\lambda_{\mathrm{norm}}$	$\lambda_{\mathrm{gate}}$
SWaT	0.02	1.00
SMD	0.01	0.05
PSM	0.01	0.1
MSL	0.01	0.05

Appendix

Table 5: Dataset-specific loss and scoring configurations.

Dataset	Channel aggregation $\mathcal{G}_{\mathcal{D}}$	Window overlap
SWaT	Mean over all variables	Mean
SMD	Top- $k$ mean	Maximum
PSM	Top- $k$ mean	Maximum
MSL	Target telemetry channel	Mean

Appendix

Table 6: Dataset-specific channel error and overlapping-window aggregation rules.

(a) SWaT
Method	A-PR	V-PR	R-F1	Aff-F1
OmniAnomaly	0.1492 $\pm$ 0.0559	0.1371 $\pm$ 0.0321	0.1718 $\pm$ 0.1140	0.7137 $\pm$ 0.0041
Anomaly Transformer	0.6829 $\pm$ 0.0193	0.4508 $\pm$ 0.0077	0.1602 $\pm$ 0.0187	0.7413 $\pm$ 0.0149
TimesNet	0.1341 $\pm$ 0.0018	0.1656 $\pm$ 0.0021	0.1593 $\pm$ 0.0057	0.7940 $\pm$ 0.0091
PatchTST	0.0890 $\pm$ 0.0002	0.0982 $\pm$ 0.0002	0.1097 $\pm$ 0.0062	0.6376 $\pm$ 0.0026
iTransformer	0.0931 $\pm$ 0.0010	0.1008 $\pm$ 0.0005	0.1277 $\pm$ 0.0029	0.6621 $\pm$ 0.0025

Appendix

Table 7: Performance of all methods on the four datasets over five independent runs. Results are reported as mean

\pm

sample standard deviation. For each dataset–metric pair, the best and second-best mean results are shown in bold and underlined , respectively.

Dataset	Variant	A-PR	V-PR	R-F1	Aff-F1
SWaT	w/o Obs.	0.6552 $\pm$ 0.0652	0.3727 $\pm$ 0.0193	0.2446 $\pm$ 0.0110	0.7047 $\pm$ 0.0012
	Concat fusion	0.7001 $\pm$ 0.1014	0.4170 $\pm$ 0.0508	0.2911 $\pm$ 0.0189	0.7270 $\pm$ 0.0090
	LEARN-TS (Full)	0.7578 $\pm$ 0.0117	0.4859 $\pm$ 0.0346	0.3102 $\pm$ 0.0436	0.7277 $\pm$ 0.0069
SMD	w/o Obs.	0.4650 $\pm$ 0.0074	0.5266 $\pm$ 0.0047	0.3222 $\pm$ 0.0070	0.8137 $\pm$ 0.0102
	Concat fusion	0.4829 $\pm$ 0.0122	0.5380 $\pm$ 0.0098	0.3262 $\pm$ 0.0057	0.8174 $\pm$ 0.0084
	LEARN-TS (Full)	0.4930 $\pm$ 0.0056	0.5398 $\pm$ 0.0037	0.3203 $\pm$ 0.0033	0.8121 $\pm$ 0.0056

Appendix

Table 8: Dataset-wise cross-modal fusion ablation. Results are reported as mean

\pm

sample standard deviation over five random seeds. The best result in each dataset–metric column is shown in bold .

(a) SWaT
Variant	Align.	Score	A-PR	V-PR	R-F1	Aff-F1
No normality	✗	✗	0.2088 $\pm$ 0.0076	0.1748 $\pm$ 0.0010	0.2971 $\pm$ 0.0611	0.7407 $\pm$ 0.0157
Scoring only	✗	✓	0.1460 $\pm$ 0.0531	0.1530 $\pm$ 0.0264	0.2711 $\pm$ 0.0635	0.7103 $\pm$ 0.0271
Alignment only	✓	✗	0.2004 $\pm$ 0.0094	0.1766 $\pm$ 0.0008	0.2646 $\pm$ 0.0211	0.7299 $\pm$ 0.0097
LEARN-TS (Full)	✓	✓	0.7578 $\pm$ 0.0117	0.4859 $\pm$ 0.0346	0.3102 $\pm$ 0.0436	0.7277 $\pm$ 0.0069
(b) SMD

Appendix

Table 9: Dataset-wise ablation of normality alignment and discrepancy-guided scoring. Results are reported as mean

\pm

sample standard deviation over five random seeds. The best result in each dataset–metric column is shown in bold .

(a) SWaT
Normality reference	A-PR	V-PR	R-F1	Aff-F1
Random semantic-free	0.7431 $\pm$ 0.0426	0.4758 $\pm$ 0.0364	0.2933 $\pm$ 0.0182	0.7276 $\pm$ 0.0017
Dataset-agnostic semantic	0.7578 $\pm$ 0.0117	0.4859 $\pm$ 0.0346	0.3102 $\pm$ 0.0436	0.7277 $\pm$ 0.0069
(b) SMD
Random semantic-free	0.4919 $\pm$ 0.0052	0.5388 $\pm$ 0.0052	0.3153 $\pm$ 0.0058	0.8022 $\pm$ 0.0143
Dataset-agnostic semantic	0.4930 $\pm$ 0.0056	0.5398 $\pm$ 0.0037	0.3203 $\pm$ 0.0033	0.8121 $\pm$ 0.0056

Appendix

Table 10: Dataset-wise comparison between random semantic-free and dataset-agnostic semantic normality references. Results are reported as mean

\pm

sample standard deviation over five random seeds. The best result in each dataset–metric column is shown in bold ; values tied at the displayed precision are both highlighted.

(a) Full-reconstruction weight $\lambda_{\mathrm{full}}$
Dataset	Value	A-PR	V-PR	R-F1	Aff-F1
SWaT	0.0	0.1766	0.1738	0.2920	0.7381
	0.1	0.7654	0.5004	0.2793	0.7121
	0.2	0.7662	0.5122	0.3293	0.7332
	0.5	0.7684	0.5139	0.3784	0.7343
	1.0	0.7665	0.5023	0.2655	0.7276

Appendix

Table 11: Dataset-wise sensitivity to (a) the full-reconstruction weight

\lambda_{\mathrm{full}}

and (b) the normality-alignment weight

\lambda_{\mathrm{norm}}

. Results are obtained using a fixed random seed. Bold rows denote the configurations used in the main experiments.

(a) Discrepancy-gating coefficient $\lambda_{\mathrm{gate}}$
Dataset	Value	A-PR	V-PR	R-F1	Aff-F1
SWaT	0.00	0.2132	0.1763	0.2607	0.7387
	0.01	0.7622	0.5137	0.2786	0.7330
	0.05	0.7640	0.5138	0.2861	0.7338
	0.10	0.7670	0.5153	0.3121	0.7337
	0.50	0.7686	0.5138	0.3299	0.7394

Appendix

Table 12: Dataset-wise sensitivity to (a) the discrepancy-gating coefficient

\lambda_{\mathrm{gate}}

and (b) the number of masked training patches. Results are obtained using a fixed random seed. Bold rows denote the configurations used in the main experiments.

Dataset	Backbone	Trainable	Peak Train GPU	Embed.	Scoring
		Params $\downarrow$	(GiB) $\downarrow$	(prompts/s) $\uparrow$	(windows/s) $\uparrow$
SWaT	GPT-2	41.16M	0.795	80.90	1111.46
	Qwen2.5-1.5B	41.75M	0.811	38.81	932.19
	Llama-3.2-1B	42.14M	0.822	53.18	958.34
PSM	GPT-2	40.79M	0.792	80.15	1194.91
	Qwen2.5-1.5B	41.38M	0.803	40.17	1152.70

Appendix

Table 13: Backbone efficiency and detection performance on four datasets. Detection results are mean

\pm

sample standard deviation over five seeds. Embedding throughput measures prompt encoding; scoring throughput excludes text encoding and uses cached embeddings. Bold indicates the best result within each dataset and column.

Figure 6: Dataset-agnostic normality prompt used in LEARN-TS. The prompt is fixed before training and shared unchanged across all datasets and input windows.

Figure 7: Abridged SWaT observation prompt template. Blue placeholders denote window-dependent descriptors and patch-index lists; dataset-level descriptions and rules remain fixed. Exact measurements and explicit anomaly labels are excluded. Repeated entries are omitted for readability.

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