WinoTS: Wavelet-based Self-Distillation for Time Series Models

Noam Major; Kathy Razmadze; Yoli Shavit

cs.LGSep 30, 2026

WinoTS: Wavelet-based Self-Distillation for Time Series Models

Authors: Noam Major, Kathy Razmadze, Yoli Shavit

Organizations: Bar-Ilan University, Ramat-Gan, Israel

Abstract

Self-supervised pre-training of time series models is currently dominated by next-token prediction and reconstruction objectives. In continuous-valued domains, these paradigms often waste model capacity on high-frequency, point-wise noise at the expense of learning invariant structure. While invariance-based self-distillation has proven highly effective in computer vision, its application to temporal data remains largely underexplored. Effectively adapting such methods to time series requires carefully designed augmentations: spatial operations like cropping can shift the timing of repeating cycles or distort the signal, while basic jittering may provide limited variation. We introduce Wavelet-based self-distillation for time series (WinoTS), an invariance-based pre-training paradigm designed specifically for temporal signals. At its core, WinoTS leverages time-frequency augmentations to construct multi-scale structural views without distorting underlying signal dynamics. Across extensive evaluations, WinoTS outperforms state-of-the-art baselines in long-term forecasting, cross-domain zero-shot transfer, and unsupervised anomaly detection. Notably, linear probing on frozen WinoTS representations frequently surpasses fully supervised models trained from scratch. Systematic ablations demonstrate that WinoTS is a flexible, architecture-agnostic framework yielding gains across time series backbones, and establish that time-frequency transformations provide a principled alternative to vision-style spatial augmentations.

Figures & tables

Figure 1 : Architectural pipeline of WinoTS . The wavelet generator produces

Q

easy and

V

hard views. The teacher processes only easy views, while the student processes both. Each teacher easy view is matched to all student views except its identical easy counterpart; every hard view is matched to all teacher views.

	Ours				Self-Supervised				Supervised
Dataset	WinoTS		WinoTS -LP		TimeSiam		TS2Vec		TimeMixer		TimeBase		SparseTSF		PatchTST		DLinear		iTransformer		FEDformer		TimesNet		Autoformer
	(ours)		(ours)		Dong et al. [ 2024 ]		Yue et al. [ 2022 ]		Wang et al. [ 2024 ]		Huang et al. [ 2025 ]		Lin et al. [ 2024 ]		Nie et al. [ 2023 ]		Zeng et al. [ 2023 ]		Liu et al. [ 2023 ]		Zhou et al. [ 2022 ]		Wu et al. [ 2023 ]		Wu et al. [ 2021 ]
	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.411	0.423	0.416	0.423	0.426	0.443	0.817	0.669	0.436	0.444	0.409	0.415	0.423	0.430	0.437	0.438	0.465	0.469	0.448	0.446	0.484	0.490	0.471	0.465	0.568	0.518
ETTh2	0.347	0.386	0.347	0.385	0.362	0.402	1.957	1.108	0.349	0.391	0.358	0.399	0.372	0.406	0.380	0.407	0.458	0.462	0.379	0.397	0.419	0.460	0.408	0.405	0.526	0.505
ETTm1	0.346	0.379	0.362	0.386	0.348	0.384	0.670	0.583	0.362	0.388	0.367	0.386	0.368	0.394	0.393	0.401	0.377	0.396	0.408	0.410	0.441	0.459	0.403	0.412	0.665	0.545

Table 1 : Average in-domain forecasting over horizons

\{96,192,336,720\}

with context length 336 (lower is better). Red and blue indicate best and second-best results. WinoTS uses full fine-tuning; WinoTS -LP freezes the backbone and trains only the forecasting head. TimeSiam and TS2Vec are self-supervised; the remaining baselines are supervised.

Backbone Family	Encoder Backbone	Avg. MSE / MAE Reduction (%)
MLP	TimeMixer	+3.2% / +2.7%
Transformer	PatchTST	+3.5% / +2.4%
	iTransformer	+2.6% / +2.9%
TCN	TS2Vec	+58.0% / +44.0%

Table 2 : Relative MSE/MAE reduction of WinoTS fine-tuning compared to training the same backbone architectures from scratch under their optimal supervised configurations. Results are averaged across five datasets (ETTh1, ETTh2, ETTm1, ETTm2, Weather) and four forecasting horizons (

H\in\{96,192,336,720\}

). Positive values indicate performance gains.

Transfer	WinoTS		WinoTS -LP		TimeMixer		TimeBase		SparseTSF		PatchTST		Autoformer		FEDformer		iTransformer		TimesNet
	(Ours)		(Ours)		Wang et al. [ 2024 ]		Huang et al. [ 2025 ]		Lin et al. [ 2024 ]		Nie et al. [ 2023 ]		Wu et al. [ 2021 ]		Zhou et al. [ 2022 ]		Liu et al. [ 2023 ]		Wu et al. [ 2023 ]
	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1 $\rightarrow$ ETTh2	0.3496	0.3888	0.3520	0.3902	0.3778	0.4000	0.3560	0.3991	0.3729	0.4075	0.3790	0.4020	0.4721	0.4810	0.4573	0.4696	0.3752	0.3991	0.4229	0.4307
ETTh1 $\rightarrow$ ETTm1	0.7027	0.5461	0.7032	0.5457	0.7866	0.5738	0.7420	0.5647	0.8111	0.5644	0.8000	0.5890	0.7730	0.5870	0.7627	0.5802	0.8307	0.5851	0.9413	0.6222
ETTh1 $\rightarrow$ ETTm2	0.2951	0.3481	0.2955	0.3482	0.3143	0.3559	0.3060	0.3611	0.3096	0.3616	0.3140	0.3570	0.3650	0.4070	0.3532	0.3902	0.3216	0.3632	0.3524	0.3837
ETTh2 $\rightarrow$ ETTh1	0.4473	0.4512	0.4513	0.4536	0.6379	0.5457	0.4150	0.4156	0.5039	0.4739	0.6410	0.5490	0.7140	0.5820	0.6856	0.5760	0.6673	0.5676	0.8393	0.6436

Table 3 : Cross-domain zero-shot forecasting transfer on ETT datasets (lower is better). Best and second-best results are highlighted in red and blue , respectively. Each source

\rightarrow

target row denotes pre-training on the source dataset followed by direct evaluation on the target dataset, without target-domain parameter updates.

Dataset	WinoTS			iTransformer			DLinear			Autoformer			TimesNet			FEDformer			Crossformer			Reformer
	(Ours)			Liu et al. [ 2023 ]			Zeng et al. [ 2023 ]			Wu et al. [ 2021 ]			Wu et al. [ 2023 ]			Zhou et al. [ 2022 ]			Zhang and Yan [ 2023 ]			Kitaev et al. (2020)
	P	R	F1	P	R	F1	P	R	F1	P	R	F1	P	R	F1	P	R	F1	P	R	F1	P	R	F1
SMD	84.04	77.34	80.55	68.22	63.79	65.93	70.13	69.34	69.73	67.91	41.63	51.62	79.28	54.20	64.39	60.86	52.23	56.22	62.99	62.32	62.65	64.03	61.56	62.77
MSL	88.47	69.39	77.78	53.62	13.79	21.94	69.45	25.38	37.18	82.14	41.25	54.92	62.38	19.03	29.17	81.72	39.34	53.11	77.20	27.22	40.25	78.22	35.51	48.85
SMAP	92.36	64.56	76.00	57.84	8.45	14.75	69.09	14.14	23.47	73.57	20.13	31.61	68.28	13.39	22.38	70.91	16.94	27.35	70.42	16.24	26.39	77.41	22.56	34.94
SWaT	34.72	8.59	13.78	6.67	1.34	2.23	5.96	1.19	1.98	9.83	1.95	3.26	4.17	0.84	1.39	10.07	2.00	3.34	28.92	7.37	11.75	9.76	1.93	3.23

Table 4 : Anomaly detection results. We report point-adjusted precision (P), recall (R), and F1-score (%). Best and second-best results are shown in red and blue , respectively.

	wavelets		jitter		jitter+crop		gaussian+crop
Dataset	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.416	0.423	0.417	0.426	0.422	0.426	0.424	0.428
ETTh2	0.347	0.385	0.349	0.387	0.349	0.388	0.360	0.392
ETTm1	0.364	0.386	0.397	0.414	0.438	0.443	0.398	0.418
ETTm2	0.252	0.308	0.247	0.308	0.253	0.312	0.274	0.328
Weather	0.238	0.273	0.242	0.278	0.303	0.320	0.263	0.294

Table 5 : Wavelet versus vision-like augmentations. We compare wavelet-based augmentations to jitter and cropping. Best results in red , second-best in blue .

Dataset	Invariance- based (Ours)		Invariance- based+MAE		MAE		NTP		JEPA
	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.416	0.423	0.418	0.424	0.465	0.465	0.428	0.435	0.442	0.447
ETTh2	0.347	0.385	0.355	0.389	0.449	0.457	0.407	0.427	0.390	0.426
ETTm1	0.364	0.386	0.370	0.388	0.349	0.387	0.346	0.381	0.375	0.393
ETTm2	0.252	0.308	0.258	0.313	0.294	0.344	0.263	0.320	0.274	0.332
Weather	0.238	0.273	0.241	0.274	0.266	0.294	0.226	0.265	0.229	0.266

Table 6 : Ablation of WinoTS ’s objective (Invariance-based self distillation). In-domain forecasting MSE/MAE averaged over horizons

\{96,192,336,720\}

. Lower is better; best in red , second-best in blue .

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Figure A1 : Qualitative comparison of wavelet-domain and vision-style view generation on a standardized periodic input window. The left panel shows the original input signal. In the middle panel, the WINO-TS easy teacher view, produced through wavelet-detail soft thresholding, and the hard student view, produced through wavelet-detail perturbation, retain the dominant period, temporal ordering, and full sequence support. Their differences are concentrated primarily in localized amplitude and high-frequency fluctuations. In the right panel, a representative vision-style augmentation pipeline based on temporal cropping, contrast modification, jitter, and additive noise alters the support and point-wise alignment of the two views, making their periodic correspondence less direct. The example illustrates the intended inductive bias of WINO-TS: preserving coarse temporal organization and global context while varying localized fine-scale content. It is provided as a qualitative sanity check rather than as general evidence of semantic preservation.

Wavelet	Family	VM	$\bm{L}$	Primary property
sym4	Symlet	4	8	Reduced phase asymmetry
sym6	Symlet	6	12	Reduced phase asymmetry
sym8	Symlet	8	16	Reduced phase asymmetry
db4	Daubechies	4	8	Minimum-phase design
db6	Daubechies	6	12	Minimum-phase design
coif2	Coiflet	4	12	Moment constraints on $\phi$ and $\psi$

Appendix

Table A1 : The WINO-TS wavelet pool

\mathcal{P}

. VM denotes the number of vanishing moments of the wavelet function

\psi

, and

L

denotes filter length in samples.

Hyperparameter	Value
Optimizer	AdamW
Base learning rate	$5\times 10^{-4}$ (linear batch scaling, $/256$ )
LR schedule	$3$ -epoch warmup $\to$ cosine to $1\times 10^{-6}$
Weight decay	cosine $0.04\to 0.1$
Gradient clipping	max-norm $3.0$
Epochs	$80$

Appendix

Table A2 : WINO-TS pre-training hyperparameters.

	Ours (synth, FT)		Ours (synth, LP)		Survey (synth, LP)
Dataset	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.417	0.425	0.521	0.489	0.438	0.446
ETTh2	0.365	0.402	0.410	0.428	0.362	0.401
ETTm1	0.351	0.378	0.361	0.389	0.357	0.384
ETTm2	0.250	0.310	0.259	0.319	0.253	0.311
Weather	0.227	0.261	0.242	0.276	0.235	0.272

Appendix

Table A3 : Forecasting (MSE/MAE, avg. over horizons

\{96,192,336,720\}

) with synthetic pre-training. Ours (FT) = best fine-tuned DINO config per dataset; Ours (LP) and survey Major and others [2026] are linear-probe. Each MSE/MAE pair is from a single run. Lower is better.

	$K{=}512$		$K{=}1024$ (Ours)		$K{=}2048$		$K{=}8192$
Dataset	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.430	0.431	0.416	0.423	0.431	0.433	0.435	0.434
ETTh2	0.369	0.395	0.347	0.385	0.377	0.399	0.378	0.400
ETTm1	0.364	0.387	0.364	0.386	0.359	0.383	0.357	0.384
ETTm2	0.248	0.311	0.252	0.308	0.248	0.310	0.249	0.309
Weather	0.238	0.273	0.238	0.273	0.269	0.298	0.241	0.275

Appendix

Table A4 : Effect of the DINO head output dimension

K

(out_dim) on in-domain forecasting (linear probe, context 336, MSE/MAE averaged over horizons

\{96,192,336,720\}

). Ours uses

K{=}1024

; best per row in bold .

		Ours				Self-Supervised				Supervised
Dataset	H	WINO-TS		WINO- TS-LP		TimeSiam		TS2Vec		TimeMixer		TimeBase		SparseTSF		PatchTST		DLinear		iTransformer		FEDformer		TimesNet		Autoformer
		(Ours)		(Ours)		Dong et al. [ 2024 ]		Yue et al. [ 2022 ]		Wang et al. [ 2024 ]		Huang et al. [ 2025 ]		Lin et al. [ 2024 ]		Nie et al. [ 2023 ]		Zeng et al. [ 2023 ]		Liu et al. [ 2023 ]		Zhou et al. [ 2022 ]		Wu et al. [ 2023 ]		Wu et al. [ 2021 ]
		MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	96	0.369	0.394	0.374	0.396	0.376	0.405	0.647	0.578	0.379	0.402	0.370	0.385	0.374	0.394	0.385	0.401	0.380	0.402	0.385	0.403	0.388	0.430	0.402	0.422	0.501	0.475
	192	0.405	0.413	0.410	0.417	0.414	0.429	0.739	0.628	0.426	0.433	0.401	0.406	0.419	0.422	0.427	0.427	0.412	0.422	0.440	0.436	0.463	0.475	0.472	0.467	0.526	0.495
	336	0.425	0.430	0.429	0.429	0.439	0.450	0.852	0.688	0.431	0.439	0.418	0.417	0.432	0.431	0.465	0.450	0.496	0.490	0.479	0.459	0.492	0.488	0.513	0.484	0.595	0.543

Appendix

Table A5 : In-domain forecasting performance per prediction length

\{96,192,336,720\}

(lower is better). All methods use the same input context length of 336. Best and second-best per row are shown in red and blue . Ours : WINO-TS (full fine-tune) and WINO-TS-LP (frozen backbone, trained head). Self-supervised : TimeSiam and TS2Vec. Supervised : end-to-end baselines.

	In-domain WINO-TS-FT		Synthetic WINO-TS-FT		Synthetic DINO+MAE
Dataset	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.411	0.423	0.417	0.425	0.418	0.423
ETTh2	0.347	0.386	0.365	0.402	0.359	0.404
ETTm1	0.346	0.379	0.350	0.378	0.345	0.377
ETTm2	0.250	0.308	0.250	0.309	0.249	0.308
Weather	0.224	0.262	0.226	0.261	0.227	0.261

Appendix

Table A6 : Effect of the pre-training data source and objective. Each entry reports MSE/MAE; lower is better. Best values for each dataset and metric are shown in bold .

Dataset	Reported samples
EthanolConcentration	261
SpokenArabicDigits	6599
FaceDetection	5890
JapaneseVowels	270
SelfRegulationSCP1	268
SelfRegulationSCP2	200

Appendix

Table A7 : Classification datasets and reported sample counts.

Dataset	WINO-TS	iTransformer
	(Ours)	Liu et al. [2023]
EthanolConcentration	0.2970	0.2810
SpokenArabicDigits	0.9900	0.9827
FaceDetection	0.6700	0.6592
JapaneseVowels	0.9570	0.9811
SelfRegulationSCP1	0.8770	0.9113

Appendix

Table A8 : Classification accuracy after full end-to-end fine-tuning initialized from a WINO-TS-pretrained backbone. Both the backbone and classification head are updated. Higher is better; best results are in bold .

Dataset	TimeMixer(MLP)		PatchTST		iTransformer		TS2Vec(TCN)
	Wang et al. [ 2024 ]		Nie et al. [ 2023 ]		Liu et al. [ 2023 ]		Yue et al. [ 2022 ]
	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.416	0.423	0.422	0.432	0.656	0.558	0.541	0.505
ETTh2	0.347	0.385	0.357	0.392	0.427	0.447	0.388	0.423
ETTm1	0.364	0.386	0.370	0.388	0.427	0.423	0.431	0.432
ETTm2	0.252	0.308	0.252	0.309	0.290	0.344	0.286	0.341

Appendix

Table A9 : Backbone ablation for WINO-TS (DINO pre-training, full augmentation family). In-domain forecasting using linear probe, with MSE/MAE averaged over horizons

\{96,192,336,720\}

. The DINO objective and augmentation are held fixed — all backbones use the full wavelet-augmentation family (pool

\{

sym4, sym6, sym8, db4, db6, coif2

\}

) — while only the encoder backbone is swapped. Lower is better; best in red , second-best in blue .

	Daub.		Zero-out		Full pool
Dataset	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.416	0.423	0.415	0.422	0.416	0.423
ETTh2	0.347	0.385	0.348	0.387	0.347	0.385
ETTm1	0.363	0.386	0.359	0.385	0.364	0.386
ETTm2	0.253	0.308	0.257	0.312	0.252	0.308
Weather	0.237	0.272	0.238	0.273	0.238	0.273

Appendix

Table A10 : Augmentation-family ablation for WINO-TS with linear probe on the DINO objective. Each cell reports MSE/MAE averaged over horizons

\{96,192,336,720\}

. Lower is better; best results are in bold .

Dataset	WINO-TS		WINO- TS+MAE		MAE		NTP		JEPA
	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.416	0.423	0.418	0.424	0.465	0.465	0.428	0.435	0.442	0.447
ETTh2	0.347	0.385	0.355	0.389	0.449	0.457	0.407	0.427	0.390	0.426
ETTm1	0.364	0.386	0.370	0.388	0.349	0.387	0.346	0.381	0.375	0.393
ETTm2	0.252	0.308	0.258	0.313	0.294	0.344	0.263	0.320	0.274	0.332
Weather	0.238	0.273	0.241	0.274	0.266	0.294	0.226	0.265	0.229	0.266

Appendix

Table A11 : Pre-training objective ablation (WINO-TS, TimeMixer backbone, full augmentation pool, Linear probe). In-domain forecasting MSE/MAE averaged over horizons

\{96,192,336,720\}

. DINO : pure self-distillation — the student matches the teacher’s centered/sharpened prototype distribution across augmented views, with no reconstruction (

\phi{=}1

). DINO+MAE : adds a masked-autoencoding term in which the student reconstructs masked patches against the raw signal, blended as

\phi\,\text{DINO}+(1{-}\phi)\,\text{MAE}

(

\phi{=}0.6

). MAE , NTP and JEPA are non-distillation baselines: pure masked autoencoding (reconstruct masked patches, no teacher) , next-token prediction (autoregressive forecasting-style pre-training) and joint-embedding predictive architecture respectively. Lower is better; best in red , second-best in blue .

	Ours		Wavelet pool				Transform
	DWT/Mixed		db		sym		SWT		MODWT
Dataset	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.416	0.423	0.416	0.423	0.416	0.423	0.415	0.422	0.417	0.423
ETTh2	0.347	0.385	0.347	0.386	0.347	0.386	0.347	0.386	0.346	0.385
ETTm1	0.364	0.386	0.364	0.386	0.365	0.387	0.360	0.385	0.364	0.386
ETTm2	0.252	0.308	0.253	0.309	0.258	0.312	0.257	0.312	0.254	0.309

Appendix

Table A12 : Wavelet augmentation ablation for WINO-TS (DINO pre-training, TimeMixer backbone, Linear probe). In-domain forecasting MSE/MAE averaged over horizons

\{96,192,336,720\}

. The first column is the default WINO-TS (DWT with the Mixed pool

\{

sym4,sym6,sym8,db4,db6,coif2

\}

) and is the shared reference. Wavelet pool fixes the DWT and sweeps the pool (db

=\{

db4,db6,db8

\}

; sym

=\{

sym4,sym6,sym8

\}

). Transform fixes the Mixed pool and swaps the transform (SWT / MODWT). Lower is better; best in red , second-best in blue .

Dataset	$\rho=0.2$		$\rho=0.4$		$\rho=0.6$		$\rho=0.8$		$\rho=1.0$
	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.428	0.430	0.464	0.458	0.416	0.423	0.419	0.426	0.465	0.458
ETTh2	0.348	0.387	0.370	0.396	0.347	0.385	0.370	0.396	0.370	0.396
ETTm1	0.371	0.391	0.383	0.403	0.364	0.386	0.382	0.404	0.384	0.405
ETTm2	0.250	0.310	0.249	0.310	0.252	0.308	0.250	0.310	0.250	0.310
Weather	0.243	0.273	0.244	0.273	0.238	0.273	0.243	0.278	0.243	0.278

Appendix

Table A13 : Shrinkage-strength (

\rho

) ablation for WINO-TS (DINO pre-training, TimeMixer backbone, DWT with the Mixed pool, Linear probe). We vary the soft-threshold shrinkage ratio

\rho

that sets the per-level denoising threshold of the teacher’s easy view: detail coefficients below

\rho\cdot\max(|\text{detail}|)

are shrunk toward zero, so small

\rho

preserves high-frequency detail (weak invariance) while large

\rho

enforces aggressive low-pass smoothing (strong invariance);

\rho=0.6

is the WINO-TS default. All other factors—transform, wavelet pool, objective, and backbone—are held fixed. Each cell reports MSE/MAE averaged over horizons

\{96,192,336,720\}

; lower is better, best per row in bold .

	$J=2$		$J=3$		$J=4$
Dataset	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.426	0.429	0.416	0.423	0.438	0.440
ETTh2	0.370	0.395	0.347	0.385	0.361	0.391
ETTm1	0.361	0.386	0.364	0.386	0.362	0.385
ETTm2	0.255	0.312	0.252	0.308	0.248	0.308
Weather	0.238	0.272	0.238	0.273	0.239	0.273

Appendix

Table A14 : Wavelet-depth ablation using linear probe. Each cell reports MSE/MAE averaged over horizons

\{96,192,336,720\}

. Lower is better; best results are in bold .

	Fixed basis		Shared sampled		Independent sampled
Dataset	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.429	0.431	0.420	0.429	0.416	0.423
ETTh2	0.369	0.394	0.363	0.391	0.347	0.385
ETTm1	0.362	0.387	0.363	0.387	0.364	0.386
ETTm2	0.247	0.308	0.275	0.328	0.252	0.308
Weather	0.243	0.279	0.243	0.279	0.238	0.273

Appendix

Table A15 : Wavelet-pool and basis-sampling ablation (Linear probe). Fixed uses a single wavelet basis for both views. Shared sampled draws one wavelet from

\mathcal{P}

and uses it for both easy and hard views. Independent sampled is the WINO-TS default, drawing

w^{\mathrm{easy}}

and

w^{\mathrm{hard}}

independently. Each cell reports MSE/MAE averaged over horizons

\{96,192,336,720\}

. Lower is better; best results are in bold .

	Default		Same-view		Symmetric
Dataset	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.416	0.423	0.435	0.434	0.420	0.430
ETTh2	0.347	0.385	0.363	0.391	0.364	0.392
ETTm1	0.364	0.386	0.361	0.385	0.362	0.385
ETTm2	0.252	0.308	0.248	0.309	0.248	0.307
Weather	0.238	0.273	0.242	0.279	0.239	0.275

Appendix

Table A16 : View-pairing ablation using linear probing. Default denotes the WINO-TS pairing rule: the teacher processes easy views, the student processes both easy and hard views, and every student view is matched to every teacher easy view except the identical easy-view index. Same-view additionally includes the matching easy–easy pairs. Symmetric allows both networks to process easy and hard views. Each cell reports MSE/MAE averaged over horizons

\{96,192,336,720\}

. Lower is better; best results are in bold .

Dataset	MSE	MAE
ETTh1	$0.417\pm 0.001$	$0.424\pm 0.001$
ETTh2	$0.350\pm 0.004$	$0.387\pm 0.002$
ETTm1	$0.361\pm 0.003$	$0.385\pm 0.001$
ETTm2	$0.248\pm 0.002$	$0.308\pm 0.002$
Weather	$0.238\pm 0.001$	$0.272\pm 0.001$

Appendix

Table A17 : Seed robustness of WINO-TS LP (DINO pre-training, TimeMixer backbone, linear probe). In-domain forecasting MSE/MAE averaged over horizons

\{96,192,336,720\}

, reported as mean

\pm

std over 5 seeds (

\{42,777,1773,2024,3407\}

). Lower is better.

Dataset	WINO-TS ( $Q{=}1,V{=}1$ )		$Q{=}2,V{=}2$		$Q{=}2,V{=}4$		$Q{=}2,V{=}6$
	MSE	MAE	MSE	MAE	MSE	MAE	MSE	MAE
ETTh1	0.416	0.423	0.433	0.433	0.429	0.431	0.429	0.430
ETTh2	0.347	0.385	0.363	0.391	0.362	0.391	0.362	0.390
ETTm1	0.364	0.386	0.361	0.385	0.362	0.385	0.362	0.386
ETTm2	0.252	0.308	0.247	0.308	0.248	0.309	0.247	0.308
Weather	0.238	0.273	0.240	0.276	0.239	0.275	0.239	0.275

Appendix

Table A18 : Multi-view ablation with a TimeMixer backbone and linear probing. The default uses

Q{=}1

easy view and

V{=}1

hard view; the alternatives use

Q{=}2

and

V\in\{2,4,6\}

. Each view uses an independently sampled wavelet basis and, for hard views, independently sampled noise. Results are MSE/MAE averaged over horizons

\{96,192,336,720\}

; lower is better and best results are in bold .

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When Does Self-Supervised Learning Transfer to Time-Series Tasks?

Noam Major, Kathy Razmadze, Yoli Shavit

Faculty of Engineering, Bar-Ilan University, Israel

Time Series Foundation Models Self-Supervised Learning Time Series Pretraining Wavelets

Open paper AskPDF arXiv

Self-supervised learning (SSL) assumes that solving pretext tasks on unlabeled data yields representations that transfer effectively across downstream applications via linear probing or fine-tuning. While this paradigm has driven major progress in vision and language, its benefits for time series remain under-investigated and often confounded by inconsistent experimental controls. To address this gap, we benchmark seven representative methods from five key SSL paradigms across anomaly detection, classification, and forecasting under parameter- and data-matched budgets. We find that transfer efficacy depends heavily on the downstream task. SSL yields substantial gains in anomaly detection and provides effective initializations for classification under fine-tuning, but offers limited to no advantage over non-pre-trained controls in forecasting. Furthermore, linear probing does not reliably predict fine-tuning performance, synthetic pre-training is often competitive with real-world corpora, and scaling encoder depth degrades forecasting accuracy. We synthesize these empirical results into practical evaluation and development guidelines for time-series SSL.

Abstract

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