VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Authors: Mouxiang Chen
Lefei Shen
Zhuo Li
Xiaoyun Joy Wang
Jianling Sun
Chenghao Liu
Venue: ICML 2025 poster (2025)
Keywords: time series forecasting
foundation models
transfer learning
Primary area: general_machine_learning->sequential_network_and_time_series_modeling
TL;DR: We propose VisionTS, a time series forecasting foundation model building from rich, high-quality natural images.

Abstract: Foundation models have emerged as a promising approach in time series forecasting (TSF). Existing approaches either repurpose large language models (LLMs) or build large-scale time series datasets to develop TSF foundation models for universal forecasting. However, these methods face challenges due to the severe cross-domain gap or in-domain heterogeneity. This paper explores a new road to building a TSF foundation model from rich, high-quality natural images. Our key insight is that a visual masked autoencoder, pre-trained on the ImageNet dataset, can naturally be a numeric series forecaster. By reformulating TSF as an image reconstruction task, we bridge the gap between image pre-training and TSF downstream tasks. Surprisingly, without further adaptation in the time series domain, the proposed VisionTS could achieve better zero-shot forecast performance than existing TSF foundation models. With fine-tuning for one epoch, VisionTS could further improve the forecasting and achieve state-of-the-art performance in most cases. Extensive experiments reveal intrinsic similarities between images and real-world time series, suggesting that visual models may offer a "free lunch" for TSF and highlight the potential for future cross-modality research. Our code is available in the https://github.com/Keytoyze/VisionTS.

VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Mouxiang Chen
Lefei Shen
Zhuo Li
Xiaoyun Joy Wang?
Jianling Sun
Chenghao Liu
Mean Square Error (MSE)
GIFT-Eval Leaderboard Cut-off date: Nov 2024
Averaged long-term time series forecasting (LTSF) datasets and four prediction lengths
Time Series Mode
few-shot
zero-shot
Image
10% downstream
downstream training dataset
training dataset
Lag-Lamma
0.5
0.4
6.1%
ETSformer
(2022)
Stationary
(2022)
Autoformer
(2021)
TimesNet
(2023)
DLinear
(2023)
PatchTST
(2023)
GPT4TS (2023)
TimeLLM (2024)
Moirals sseell (2024)
Moirak (2024)
VisianTS
(ours)
Moiral (5)
TimesFM
Moirai (8)
Chronos (S)
Chronos
game began
development 2010,
Moirai (L)
Pre-training
carrying over large portion of the work...
Chronos (L)
Dataset
No Pre-training
Natural Texts
Time-Series
Natural Images (no time-series training))
VisionTS
0.75
1.00
Norm. MASE
Figure Loon-term forecasting (left) and GIFT-Eval (right) performance comparison. Our VISIONTS, without any training on time series data, outperforms the pure time series foundation models in the zero-shot setting.
Abstract
mulating TSF image reconstruction task, we bridge the gap between image pre-training and TSF downstream tasks. Surprisingly, without further adaptation in the time series domain, the proposed VISIONTS could achieve better zero- shot forecast performance than existing TSF foun- dation models. With fine-tuning for epoch, VISIONTS could further improve the forecasting and achieve ofthe-ofthhe-att performance in most cases. Extensive experiments reveal intrinsic sim- ilarities between images and real-world time se- ries, suggesting that visual models may offer a "free lunch" for TSF and highlight the potential for future moos-mtodality research. Our code is publicly available at https: ////tgtttt tighub.. com/ Deytoyze/Visiion5s
Foundation models have emerged as a promising approach in time series forecasting (TSF). Exist- ing approaches either repurpose large language models (LLMs) build large-scale time series datasets to develop TSF foundation models for universal forecasting. However, these methods face challenges due to the severe cross-domain gap or in-domain heterogeneity. This paper ex- plores new road building a TSF foundation model from rich, high-quality natural images. Our key insight that a visual masked autoeneoder, pre-trained the ImageNet dataset, natu- rally be numeric series forecaster. By refor-
'Zhejiang University Staate Street Technology (Zhejiang) Ltd "aleesforce Research Asia. Correspondence to: Chenghao Liu <chenghao. liu@ salesforce.com. Zhuo Li <lizhuo@ zju.edu.cn>.
1. Introduction
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).
Foundation models (Bommasani et al., 2021) have revolu- tionized natural language processing (NIP) and computer

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VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
vision (CV) in recent years (Brown et al., 2020; He al., 2022). By pretraining on large-scale data, they have shown remarkable few-shot and even zero-shot performance across various downstream tasks. This has motivated an emergent paradigm shift in time series forecasting (TSF), moving from a traditional one-model per-datsee framework to uni- versal forecasting with a single pre-trained model (Woo et al., 2024; Goswami et al., 2024). TSF foundation model can greatly reduce the need for downstream data and demonstrate strong forecasting performance on diverse domains, such as energy consumption planning, weather forecasting, and traffic flow.
Trend
Seasonality
Semie M /
Geyaclle
We have recently witnessed two roads to building a TSF foundation model for universal forecasting. The first tries to repurpose large language models (LLMs) that have been pre-trained on text data for TSF tasks (i.e,, text-based) (Zhou et al., 2023; Jin et al., 2024), based on the observation that LLMs and TSF models share similar left-to-right forecasting paradigm. However, due to the significant gap between these two modalities, the effectiveness of such transferability between language and time series has recently been questioned by Tan et al. (2024).
on Stationarity
Posi
Sudden Change
The second road focuses constructing large-scale time- series datasets collected from diverse domains to train a TSF foundation model from scratch (i.e., time series- based or TS- based) (Woo et al., 2024; Das et al., 2024). Nevertheless, unlike images or language with unified formats, time series data is highly heterogeneous in length, frequency, number of variates, domains, and semantics, limiting the transferabil- ity between pre-training and downstream domains. Until recently, constructing high-quality dataset remains chal- lenging and is still in the early exploration stage.
An image of the ImageNet dataset (Deng al., 2009), in which the pixel arrays can display many well well-known features of real-workd time series, such as trend, seasonality, and stationarity (Qiu et al., 2024). By self-supervised pre-training on ImageNet, it is reasonahle that visual model could understand these features and exhibit a level of time series forecasting ahility.
Figure 2.
We focus on visual masked autoencoder (MAE)', a popular CV foundation model (He et al., 2022) by self-supervised pre-training on ImageNet (Deng et al., 2009). As an image reconstruction and completion model, MAE can naturally be numeric series forecaster. Inspired by the well-known prompt technique in NLP (Schick & Schüitze, 2021), we propose simple method to reformulate TSF as a patch- level image reconstruction task to bridge the gap between pre-training and downstream tasks. Specifically, we trans- form ID time-series data into 2D matrices via segmentation. Then, we render the matrices into images and align the fore- casting window with masked image patches. This allows us to make zero-shot forecast without further adaptation.
In this paper, we investigate a third road that is less explored yet promising: building TSF foundation models with pre- trained visual models. Our key idea that pixel variations in natural image can be interpreted as temporal sequences, which share many intrinsic similarities with time series: Similar modalities: Unlike discrete texts, both images and time series are continuous; Similar origin: Both time series and images are observations of real-world physical systems, whereas languages are products of human cognitive processes; Similar information density: Languages are human-generated signals with high semantic density, while images and time series are natural signals with heavy redundancy (He et al., 2022); and 4 Similar features: As shown in Section 1, images often display many features of real-world time series, which are rarely found in language data. Based on these findings, images could be a promising modality for transferring to TSF. We are motivated to answer the question: Can a visual model pre-trained on images be free-lunch foundation model for time series forecasting?
We evaluate our proposed VISIONTS on large-scale bench- marks, including 8 long-term TSF (Zhou et al., 2021). 29 Monash (Godahewa et al., 2021), and 23 GIFT-Eval (Aksu et al., 2024) datasets, spanning diverse domains, frequencies, and multivariates. To the best of our knowledge, the scale of our evaluation benchmark the largest among exist- ing TSF foundation models. As demonstrated in Fig. 1, without further adaptation on time series, vanilla MAE can
We fonts to distinguish MAE (Masked Autoencoder) and MAE (Mean Absolute Error) this paper.
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VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
surprisingly achieve comparable performance or even out- perform the strong zero-shot TSF foundation models. By fine-tuning MAE in each downstream dataset for single epoch, VISIONTS can lead to SOTA performance in most long-term TSF benchmarks.
each with width and height of s w/n. During pre- training, some random patches are masked, while the re- maining visible patches are fed into the ViT with their posi- tion encodings. MAE are trained to reconstruct the masked pixel values from these visible patches.
To further understand and explain the transferability, we use an MAE encoder to visualize both modalities, showing level of similarity between time series and natural image representations. Additionally, heterogeneity within time-series data across domains, and images can serve as bridge to connect these isolated time-series representations. This could further explain why VISIONTS performs better than some cross-domain TSF models. Our findings suggest that time series and natural images may be two sides of coin, and visual models can be a free lunch for time series forecasting. We hope our findings inspire future cooss-modal research on CV and TSF.
3. Methodology
As noted in the Introduction, time series and images share intrinsic similarities,, suggesting the transfer potential of pre-trained visual models (particularly MAE in this paper) for TSF. To reformulate TSF tasks into MAE's pre-training task, our high-level idea is straightforward: map the look- back/forecasting windows to visible/masked patches, re- spectively. This idea is supported by the prompt tuning (Schick & Schütze, 2021) in NLP, where the predictions for [mask] token in pre-trained language models, e.g., BERT (Devlin et al., 2019), are directly used for downstream tasks. By unifying the forms of the two tasks, we bridge the gap between the two modalities without further training.
observe considerable
Our contributions are summarized as follows:
However, implementing this idea poses a challenge: the dimension of time-series data (ID) is different from im- ages (2D). Moreover, the size of images in the pre-training dataset is fixed at 224 x 224, while the lengths of time series data can vary dynamically. In the following, we describe the details of VISIONTS to address this challenge. Our architecture is depicted in Fig.
We explore a road to building TSF foundation model from natural images, conceptually different from the ex- isting text-based and TS-based pre-training methods.
We introduce VISIONTS, a novel TSF foundation model based on a visual MAE. To bridge the gap between the two modalities, we reformulate the TSF task into an image reconstruction task.
Comprehensive evaluations of VISIONTS on large-scale benchmarks across multiple domains demonstrate its sig- nificant forecasting performance, surpassing few-shot text- based TSF foundation models and achieving comparable or superior results to zero-shot TS-based models.
Segmentation Given a univariate input X E R2, the first goal is to transform into 2D matrix. We propose to segment it into [4/p] subsequences of length P, where P is the periodicity. Notably, when the time series lacks clear periodicity, we can set P = 1 directly, which is also effective in our experiments (Appendix B.6). In practice, P can be determined using statistical methods like Fast Fourier Transform (Wu et al., 2023; Chen et al., 2024) or domain knowledge like sampling frequency (Godahewa et al., 2021; Alexandrov et al., 2020). In this paper, we select P based the sampling frequency, elaborated in Appendix A.2.
2. Preliminaries
Time Series Forecasting (TSF) For a multivariate time series with M variables, let x, E RM represent the value at t-th time step. Given a historical sequence (i.e., look- back window) X,-Lat = i., _ E XR with context length L, the TSF task is predict future values (i.e., forecast horizon) with prediction length H: X
After that, these subsequences are then stacked into a 2D matrix, denoted by Iraw E I [x()), This encoding strat- egy is proven to be efficient by recent work like TimesNet (Wu et al., 2023) and Sparse (Lin et al., 2024), as it al- lows for the simultaneous capture of both variations within the same period (i.e., intra-period) and across periods with the same phase (i.e., inter- pettod- Moreover, it ensures that each element in Inw and its neighbors align with the spatial locality property of images (Krizhevsky et al., 2012), where nearby pixels tend to be similar due to the inherent cohesiveness of objects in the real world. Therefore, this further narrows the gap between time series and images.
[x,, -11 E R
Patch-Level Image Reconstruction To obtain high- quality visual representation for downstream CV tasks, He et al. (2022) proposed masked autoencoder (MAE) to pre- train a Vision Transformer (ViT) (Dosovitskiy et al., 2021) using a patch-level image reconstruction task on ImageNet. Specifically, for an image of size W x W (where W rep- resents both the width and height, as ImageNet images are square), the image is evenly divided into N x N patches,
3

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VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
2 render
look-back window
①
segmentation
time
time
periods
perlods
time
Resize image through interpolation
forecasting
reconstruction
3 alignment
reconstructed patches
patch-level
time
image reconstruction
N
reverse
timo
Visual MAE
visible patches
Figure 3. VISIONTS architecture. The input is first segmented by period, rendered into a grayscale image, and then aligned with the visible patches on the through resampling. MAE is used predict the masked patches on the right, and the reconstructed image is then reversed to forecasting.
Normalization MAE standardizes each image based the mean and standard deviation computed on ImageNet. Therefore, we apply instance normalization to Iaw. which also a standard practice in current TSF (Kim et al., 2022). Notably, we observed that normalizing Law to a standard de- viation r, where r hyperparameter less than yields superior performance. One explanation that the magni- tude of inputs/outputs during MAE pretraining is constrained by the limited range of color values. Therefore, reducing the magnitude of Iaw prevents exceeding these limits. However, an excessively low r can result in values that are difficult to distinguish. We found that a moderate value (0.4) of r performs well across most scenarios (See Appendix B.9 for more details). Let nnrm denote the normalized matrix, which is computed follows:
size of I Tgeyy we propose resize Tgey align with the pre- training data. Formally, let the total number of 2D patches used in pre-trate pre-training be N x N and the size of each patch be S S. We set the number of visible patches to x and the masked patches to N x (N - -n),, where n = [NV 1/(L+H)] is determined by the ratio of context length to prediction length H. We resample the image ggcy adjust the size from the original dimensions (P, ['/rl) to (N S, n. S,,,S), making it more compatible with MAE. We select bilinear interpolation for the resampling process.
Moreover, we found that reducing the width the visi- ble portion can further improve performance. One possi- ble explanation is that MAE uses a large masked ratio dur- ing pre-training, with only 25% of patches visible. Re- ducing the image width may align the masked ratio more closely with pre-training. Therefore, we propose multiply- ing n by hyperparameter EE [0, Similar to r, we found that setting e = 0.4 performs well in our exper- iments (See Appendix B.9). This can be formulated as n n= N-- 4/(++))
I Inom Irw Mean Iraw
Doaidard-Deviation(A. (Iaw))
Rendering Since each image has three channels, we sim- ply render Inom as grayscale image Igey Px 4/r] x3 where all three channels are identical norm This choice added
Reconstruction and Forecasting After obtaining the MAE-rreonsttructed image, we simply reverse the previous steps for forecasting. Specifically, we resize the entire image back to the original time series segmentations through the same bilinear interpolation, and average the three channels to obtain single-channel image. After de-normalizing and flattening, the forecasting window can be extracted.
purely result-driven: In early experiments, convolutional layer with three output channels to convert the grayscale image into color image and then fine-tuned to find the optimal color transformation, which, however, did not significantly improve the performance.
Alignment Our goal is to predict the columns on the right of Igry to forecast the future sequence. A straightforward approach is to treat grey as the visible left portion and the predicted columns as the masked right portion. However, since the image size during pre-training may not match the
Discussion on Multivariate Forecasting In addition to the temporal interactions, multivariate time series data some- times show interactions between variables. While pre-

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VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Table Zero-shot or few-shot results the long-term TSF benchmark. Results are averaged across prediction lengths (96, 192, 336, 720), with full results in Table 9 (Appendix B.2). Bold: the best result.
Zero-Shot
Few-Shot (10% In-distribution Downstream Datasel)
Pretrain
Images
Time series
Text
No Pretrain
Method
VISIONTS
MOIRAIS
MOIRAIs
MOIRAI,
TimeLLM
GPT4TS
DLinear
PatchTST
TimesNet Autoformer Informer
MSE
0.390
0.400
0.434
0,510
0.556
0.590
0.691
0.633
0.869
0.702
1.199
ETTh1
MAE
0,414
0.424
0.439
0.469
0,522
0.525
0.600
0,542
0.628
0,596
0.809
MSE
0,333
0.341
0,346
0.354
0.370
0.397
0.605
0,415
0.479
0.488
3,872
ETTh2
MAE
0.375
0.379
0,382
0.377
0.394
0.421
0.538
0.431
0.465
0.499
1,513
MSE
0.374
0.448
0,382
0.390
0.404
0.464
0.411
0.501
0.677
0.802
1.192
ETTml
MAE
0.372
0.410
0.388
0.389
0.427
0.441
0.429
0.466
0.537
0.628
0.821
MSE
0.282
0.300
0.272
0.276
0.277
0.293
0.316
0.296
0.320
1.342
3,370
ETTm2
MAE
0.321
0.341
0.321
0.320
0.323
0.335
0.368
0.343
0.353
0.930
1.440
MSE
0.207
0.233
0.188
0.188
0.175
0.176
0.180
0,180
0.323
0.431
1.195
Flectricity
MAE
0.294
0.320
0.274
0.273
0.270
0.269
0.280
0,273
0.392
0.478
0.891
MSE
0.269
0.242
0.238
0.260
0.234
0.238
0.241
0.242
0.279
0,300
0.597
Weather
MAE
0.292
0.267
0.261
0.275
0.273
0.275
0.283
0.279
0.301
0.342
0.499
MSE 0.309
0.327
0.310
0.329
0.336
0.360
0.407
0.378
0.491
0.678
1.904
Average
MAE
0.345
0.357
0.344
0.350
0.368
0.378
0.416
0,389
0.446
0.579
0.995
1st count
0
Norm. MASE
TS-based (zero-shot)
1.0
in-distribution (full-shot)
Norm. MAE
1.0
Image-based (zero-shi shot)
cross-domain (zero-shot)
0.8
0.8
0.6
Lag Lla-
Timer
TTMs
TimesFM
Moiral (5)
Moiral (B)
Chronas (5)
Chronas (B)
Moiral (L)
Chronos (L)
VisionTs
LLMTime
SES
Theta ARIMA
ETS
PR N: N-BEATS
Traf CafBoost DeepAr TBATS WaveNet FFNN VisionTs Moirai(S)
Figure 4. Performance the Gval-Eval Leaderboard (cut-off at VISIONTS's 's release).
Figure 5. Aggregated results on the Monash TSF Benchmark, with full results in Table (Appendix B.6).
trained vision models effectively capture temporal interac- tions based on the intrinsic similarities between images and time series, they struggle to capture vnter-variable interac- tions due to a limited number of image channels, especially without further training. Fortunately, recent work shows that channel independence forecasting each variable sep- arately can be effective and is widely used in recent deep forecasting models (Nie et al., 2022; Han et al., 2024; Jin et al., 2024; Zhou et al., 2023; Lin et al., 2024). Following these works, we adopt channel independence our paper while leaving the exploration of capturing inter-variable interactions to future work.
backbone by default. Baseline and benchmark details are elaborated in Appendix A.1.
4.1. Zero-Shot Time Series Forecasting
We first evaluate VISIONTS's zero-shot TSF per- formance without fine-tuning on time-series modalities. To prevent data leakage, we selected six widely-used datasets from the long-term TSF benchmark that are not included in MOIRAI'S 's pre-training set for evaluation. Since most base- lines cannot perform zero-shot forecasting, we report their few-shot results by fine-tuning on the 10% of the individual target datasets. We also evaluate the Monash benchmark and GIFT-Eval benchmark. Notably, the Monash bench- mark is more challenging for VISIONTS since they were used in MOTRAT'S pre-training but not for VISIONTS. We set the hyperparameters to = = 0.4. Following common practice (Zhou et al., 2023; Woo et al., 2024), we conduct hyperparameter tuning on validation sets to determine the optimal context length I,, detailed in Appendix B.
Setups
4. Experiments
We follow the standard evaluation protocol proposed by Woo et al. (2024) to test our VISIONTS on 35 widely-used TSF benchmarks, and additionally evaluate it on the GIFT- Eval (Aksu et al., 2024) which is the largest TSF benchmark for zero-shot foundation models. We use MAE (Base) as our
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VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Table Average MSE of different MAE variants, with full results in Table 17 (Appendix B.7).
Table 3. Computational cost in terms of seconds for forecasting a batch of 32 time series data.
Base
Large 330M
Huge 657M
Context Length
1k
1k
3k
4k
112M
Prediction Length
1k
2k
3k
4k
1k
ETTh1
0.390
0.378
0.391
PatchTST
0.01
0.01
0.01
0.01
0.01
0.02
0.03
0.04
ETTh2
0.333
0.340
0.339
DeepAR
0.26
0.32
0.37
0.43
0.26
4.06
6.10
8.17
ETTml
0.374
0.379
0.383
GPT4TS
0.01
0.01 0.01
0.02
0.01
0.03
0.04
0.06
ETTm2
0.282
0,286
0.284
MOIRAlRas
0.03 0.04 0.04
0.05
0.03
0.04
0.05
0.06
Electricity
0,207
0.209
0.202
TimesFM
0.08 0.14 0.20 0.27
0.07
0.13
0.20 0.25
Weather 0.269
0.272
0.292
LLMTime (8B)
> 200
> 200
Avg. 0.309
0,311
0.315
VISIONT'S (c 0.4) 0.04 0.03 0.03 0.03 0.04 0.04 0.05 0.05
Results on Long-Term TSF Benchmark Table shows that VISIONTS surprisingly achieves the best forecasting performance in most cases (7 out of 14), Specifically, VISIONTS demonstrates a relative average MSE reduc- tion of approximately 6% compared to MOIRATSmal and MOIRAlLagee- and performs comparably to MOIRAlBaSe- When compared to the various few-shot baselines, VI- SIONTS shows a relative average MSE reduction ranging from 8% to 84%. Given that all baselines except for VI- SIONTS are trained on the time-series domain, this result is particularly encouraging. It suggests that the transferabil- ity from images to time-series stronger than from text to time-series, and even comparable to the in-domain transferability between time-series. We also include comparison with two TSF foundation models, TimesFM (Das et al., 2024) and LLMTime (Gruver et al., 2023), in Ap- pendix B.3, as well as traditional algorithms (ETS, ARIMA, and Seasonal Naïve) in Appendix B.4. Results show that VISIONTS still outperforms all of these baselines.
promising result highlights VisionTS's strong zero-shot fore- casting ability and effective cross- cross-mddality transferability.
4.2. Further Analysis of VISIONTS
Backbone Analysis In Table 2 (full results in Ap- pendix B.7), we observe that the overall performance of three MAE variants (1 (112M, 330M, and 657M) outperforms MOIRAlSmal and MOIRAl Particularly, larger models show a slight decrease in performance. This may be due to larger visual models overfitting image-specific features, reducing their transferability. A similar phenomenon was reported in MOIRAI, where larger models were found to degrade performance. We leave the exploration of scaling laws in image-based TSF foundation models for the fu- ture. Additionally, to explore the potential with other vision models, we also test LaMa (Suvorov et al., 2022), a visual inpainting model. Results in Appendix B.7 demonstrate that VISIONTS with LaMa performs similarly to MOTRAT in the zero-shot setting. This suggests that the performance is driven by the inherent similarity between images and time series, not solely by the MAE model.
Results on GIFT-Eval Benchmarks Fig. 4 shows the comparison of VISIONTS with six previously published TSF foundation models on the GIFT-Eval TSF Leader- board?, where VISIONTS surprisingly ranked first in terms of normalized MASE. It should be noted that although some concurrent works (after the release of VISIONTS) in the cur- rent leaderboard outperform VISIONTS, there may be data leakage issues for these works. In contrast, visual MAE was trained on ImageNet, long before the release of GIFl-Eval leaderboard, which can ensure no leakage.
Computational Cost We evaluate the computation cost of different baselines on an NVIDIA A800 GPU. Results are averaged on 90 runs. Table 3 shows the results between various TSF foundation models, showing that VISIONTS are comparable to MOIRAlBase and GPT4TS and faster than TimesFM, which is an ruto-regressive model. While com- putation time increases with context length for all the other Transformer- basedd baselines, VISIONTS remains nearly constant. This is because VISIONTS encodes input se- quences into an image with constant size, ensuring ((1) efficiency. In contrast, Transformer-basee methods operate at O(L?) relative to context length L.
Results Monash Benchmark Fig. 5 shows the re- sults aggregated from 29 Monash datasets, showing that VISIONTS in the zero-shot setting surpasses all models individually trained on each dataset and significantly out- performs the other cross- baseline (i.e., LLMTime). achieves second place among all baselines, just behind MOIRAI that pre-trained on all the training datasets. This
Hyperparameter Analysis Appendix B.9 illustrates the impact of three hyperparameters. For context length L, as shown in Fig. 6, performance typically improves with increasing I., particularly on high-frequency datasets like
6

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VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Weather
ETTm1
0.42
0.32
0.40
0.39
0.28
0.38
1000
2000
3000 4000
1000
2000
3000 4000
(a) Weather
(b) ETTml
ETTm2
Electricity
0.24
0.30
0.23
Monash
0.29
0.22
Weather
0.21
ETTm1
0.28
1000
2000
3000
4000
1000
2000 3000 4000
Electricity
ImageNet
(c) ETTm2
(d) Electricity
Figure 6. MSE (Y-axis) performance of different context lengths L (X-axis), averaged on four prediction lengths.
Figure 7. Modality visualization of the images (ImageNet) and time series (Monash, Weather, Electricity, and ETTml) based the MAR,, encoder.
Table 4. Aggregated full-shot forecasting performance on eight long-term TSF benchmarks (ETThl, ETTh2, ETTml, ETTm2. Illness, Weather, Traffic, Electricity). VISIONTS fine-tuned only a single epoch on each dataset except for Illness. Due the space limit, we report the 1d count for each baseline, with full results in Table 21 (Appendix C.2).
Pretrain
Images
Text
No Pretrain
Method
VISIONTS 46
Time-LLM
GPT4TS
Dlinear
PatchTST
TimesNet FEDformer Autoformer Stationary
ETSformer Informer
1st count
4
12
19
0
0
Weather (10-minute frequency) and ETTm m/ (15- minute frequency). This aligns with other TSF foundation models like MOTRAT. As for the normalization constant and alignment constant when both of them are around 0.4, performance is generally well across most benchmarks.
Ablation Study We conduct experiments validate our choices in the Alignment step, detailed in Appendix B.8. First, we test three different interpolation strategies, which shows that Bilinear interpolation performs best. Sec- ond, we apply horizontal and vertical flips on the image to examine whether the assumed left-to-right, top-to-bottom order is efficient. Results show that these changes do not significantly affect performance, suggesting that image re- construction is isotropic and not influenced by certain orientation.
Modality Analysis: Where does the zero-shot forecasta- bility come from? We further examine the gap between time series and images to explain the transferability of zero- shot forecasting. We sampled 1,000 images from ImageNet- 1k and 300 samples from each time series dataset. We fed them into the MAE, maintaining consistent image mask across all data. Fig. 7 visualizes the MAE encoder outputs of these data, which are flattened and reduced to 2-dimension by t-SNE. Notably, some time series, such as ETTml and Electricity, fall within the ImageNet distribution. It sug- gests a relatively small gap between images and some time series (e.g., Electricity and ETTml), which could explain the good transferability. Additionally, while Ima- geNet displays concentrated distribution, time series are generally more scattered. For instance, ETTml clusters in the upper right, whereas Monash is found in the lower left, with a significant gap. This indicates strong heterogene- ity within time series data and suggests that images may serve bridge to connect isolated time series modality.
Qualitative Analysis: When does VISIONTS perform well, and when does it not? In Appendix D, visualize the zero-shot forecasting of VISIONTS alongside the in- put and reconstruction images, highlighting both successful cases (where VISIONTS outperforms MOIRAI) and failures (where MOIRAI prevails). When the input exhibits strong regularity (Fig. 11), VISIONTS effectively forecasts both the periodicity (via segmentation) and trends (via MAE's capabilities). In contrast, MOIRAI, akin seasonal naïve methods, struggles to capture inter-period trends. For less- structured input (Figs. 12 to 14), MOIRAI adopts conserva- tive approach with lower volatility to minimize errors, while VISIONTS takes more aggressive stance. This strategy oc- casionally yields more accurate trend predictions (Figs. 12

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VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
and 13) but may also result in greater MAE (Fig. 14).
5. Related Work
Depending on the pre-training data, TSF foundation models can be categorized into Text-based and TS-based models, We first review related works and then introduce recent research on image-based time series analysis.
4.3. Full-Shot Long-Term Time Series Forecasting
Setups We evaluate the full-shot capability of each base- line trained on individual long-term TSF benchmarks. In addition to the six datasets used for zero-shot forecasting, we also include the popular Traffic and Illness datasets. As self-attention and feed-forward layers contain rich knowl- edge that can be transferred to TSF, we choose to fine-tune only the layer normalization (LN) layers while freezing the other parameters, which is also adopted by Zhou et al. (2023). Training details are elaborated in Appendix C.I.
Text-based TSF Foundation Models Large Language Models (LLMs) pre-trained on large amounts of text data are being applied to TSF tasks. For example, Zhou et al. (2023) fine-tuned a pre- pre-trained GPT (Radford et al., 2019) on each time-series downstream task, such as forecasting, classification, imputation, and anomaly detection. Based on Llama (Touvron et al., 2023), Jin et al. (2024) froze the pre- pre-trained LLM and reprogrammed the time series to align with the language modality. Bian et al. (2024) adopted two-stage approach by continually pre-training GPT (Rad- ford et al., 2019) on the time-series domain. Nevertheless, the TSF performance of LLMs has recently been questioned by Tan et al. (2024), which designed several ablation studies to show that textual knowledge is unnecessary for forecast- ing. In this paper, we attribute it to the large modality gap. Some recent approaches focus on directly transforming the time series into natural texts for LLMs, allowing for zero- shot forecasting. For example, PrompiCast (Xue & Salim, 2023) used pre-defined templates to describe numerical time series data, while LLMTime (Gruver et al., 2023) directly separated time steps using commas and separates digits us- ing spaces to construct the text input. However, due to the efficiency issue of the autoregressive decoding strategy and the expensive inference cost of large language models, their practical use is limited.
Main Results Table 4 summarizes the full-shot results, with the full results and standard deviations detailed in Ap- pendix C.2. It shows that VISIONTS outperforms other baselines in most cases (46 out of 80), surpassing the non- pretrained PatchTST and the sanguage-pretrinne GPT4TS. Remarkably, except for Illness with the least data, VI- SIONTS demands only a single epoch of fine-tuning, This suggests that even minimal fine-tuning enables Vision'TS to adapt to time series effectively. Compared with Table 1, fine-tuning provides limited benefits for ETTh and ETTh2 but significantly improves other datasets. We attribute this to the smaller data scale of ETTb1 and ETTh2.
Ablation Study Tan et al. (2024) proposed several abla- tion variants for text-based foundation models, including w/o LLM (removing the LLM), LLMZAttn/LLLP2T (replacing the LLM with a single ietf--ttenniionTransffnnn layer), and RandLLM (randomly initializing the LLM). They found no significant performance differences and con- cluded that textual knowledge is unnecessary for TSF. We conducted similar ablations to assess the role of the vision model (VM), including w/o VM, VM2Attn, VM2Trsf, and Rand Appendix C.3 shows that these variants lead to worse performance, indicating that visual knowledge is beneficial for TSF.
Time Series-Based TSF Foundation Models Self- supervised pre-training a TSF model on the same dataset used for downstream TSF tasks is a well-explored topic (Ma et al., 2023; Zhang et al., 2024), such as denoising autoencoders (Zerveas et al., 2021) or contrastive learning (Woo et al., 2022a; Yue et al., 2022). They follow a similar paradigm to the masked autoencoder (MAE) in computer vi- sion, which is well-studied topie in other machine learning fields, such as BERT (Devlin et al., 2019), CBraMod (Wang et al., 2025), and HuBERT (Hsu et al., 2021). However, these methods rarely examine the cross-dataset generaliza- tion capabilities. Recently, research has shifted towards training universal foundation models, by collecting large- scale time series datasets from diverse domains (Ansari et al., 2024; Goswami et al., 2024; Liu al., 2024; Das et al., 2024; Dong et al., 2024; Feng et al., 2024) or generating numerous synthetic time series data (Fu et al., 2024; Yang et al., 2024). As a representative method, Woo et al. (2024) collected 27 billion observations across nine domains and trained TSF foundation models of various scales, achieving strong zero-shot performance. However, given the severe
Analysis: Fine-tuning strategies As stated before, we fine-tune only the layer normalization (LN). We also tested fine-tuning the bias, MLP, or attention layers, in addition to full fine-tuning and freezing. All hyperparameters were kept constant. Note that freezing differs from the previous zero-shot experiment, where a longer context length was used. Appendix C.3 show that fine-tuning LN is the best. Modifying MLP or attention layers results in significant performance drops, suggesting that valuable knowledge resides in these components.
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heterogeneity, constructing high-quality large datasets poses significant challenges for building these foundation models.
also like to thank reviewers for their valuable comments.
Impact Statement
Image-Basee Time-Series Analysis Previous research has investigated encoding time series data into images and used convolutional neural networks (CNNs) trained from seratch for classification (Wang & Oates, 2015a;b; Hatami al., 2018) forecasting (Li et al., 2020; Sood et al., 2021; Semenoglou et al., 2023). Recent researchers explored us- ing pre-trained models for these imaging time series. Li et al. (2024) used a pre-trained vision transformer (ViT) for classification. Wimmer & Rekabsaz (2023) and Zhang et al. (2023) employed vision-language multimodal pre-trained models to extract predictive features and generate text de- scriptions. Yang et al. (2024) generated synthetic time series data to pre-train vision model for the TSF task. However, these studies did not deeply examine the transferability from natural images to TSF. Despite early efforts by Zhou et al. (2023) to fine-tune BEiT (Bao et al., 2022) trained on images for time series forecasting, it still falls short of the leading text-based and TS-based TSF foundation models. To the best of our knowledge, we the first show that image-based foundation model, without further time-series adaptation, can match or even surpass other types of TSF foundation models.
This paper presents work whose goal is to advance the field of time series forecasting. There are many potential societal consequences of our work, none of which we feel must be specifically highlighted here.
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In this paper, explore novel approach to building time series forecasting (TSF) foundation model using natural images, offering new perspective distinct from the tradi- tional text-based and TS-based methods. By leveraging the intrinsic similarities between images and time series, we in- troduced VISIONTS, an MAE-based TSF foundation model that reformulates the TSF task as an image reconstruction problem. Our extensive evaluations demonstrate that VI- SIONTS achieves outstanding forecasting performance in zero-shot and full-shot settings, being free lunch for TSF foundation model. We hope our findings could open new avenues for further cross-modality research.
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Acknowledgments
Research work mentioned in this paper is supported by State Street Zhejiang University Technology Center. We would

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Appendix
A. Details of Experiments
A.I. Benchmark and baselines
Long-Term TSF Benchmark We evaluate our model on 8 widely used long-term TSF datasets (Zhou et al., 2021; Wu al., 2021), including ETTh1, ETTh2, ETTmI, ETTm2, Electricity, Traffic, Illness, and Weather. Performance is assessed using Mean Squared Error (MSE) and Mean Absolute Error (MAE), with lower values indicating better forecasting accuracy.
Monash Benchmark Following Woo et al. (2024), we tested 29 Monash datasets (Godahewa al., 2021) using GluonTS (Alexandrov et al., 2020), including M1 Monthly, M3 Monthly, M3 Other, M4 Monthly, M4 Weekly, M4 Daily, M4 Hourly, Tourism Quarterly, Tourism Momthly, CIF 2016, Australian Electricity Demand, Bitcoin, Pedestrian Counts, Vehicle Trips, KDD Cup. Weather, NN5 Daily, NN5 Weekly, Carparts, FRED-MD, Traffic Hourly, Traffic Weekly, Rideshare, Hospital, COVID Deaths, Temperature Rain, Sunspot, Saugeen River Flow, and US Births. Performance is assessed using MAF.
GIFT-Eval Benehmark Aksu et al. (2024) introduces the General Time Series Forecasting Model Evaluation, GIFT-Eval, encompasses 23 datasets over 1 144,000 time series and 177 million data points, spanning seven domains, 10 frequencies, multivariate inputs, and prediction lengths ranging from short to long-term forecasts. We use a constant context length 2,000 for VISIONTS and we report the point forecast performance using MAPE,
Baselines We select representative baselines for comparison, including TS-based and Text-based foundation models, and other popular TSF baselines covering both cansformer-based. MI.P-based and CNN-based architectures. The baseline models selected for comparison are briefly described below:
1.
MOIRAI (Woo et al., 2024) is TSF foundation model trained on the Large-scale Open Time Series Archive (LOTSA), with over 27B observations across nine domains. It has three variants: small, base, and large.
2. TimesFM (Das et al., 2024) is a decoder-style TSF foundation model, using a large time-series corpus comprising both real-world and synthetic datasets.
Time-LLM (Jin et al., 2024) is text-based TSF foundation model built on Llama, which reprograms time series data to align with the language modality, keeping the LLM frozen.
4. GPT4TS (Zhou et al., 2023) (OneFitsAll) is another text-based model based on GPT, fine-tuned for forecasting tasks.
LLMTime (Gruver et al., 2023) encodes time series data to a text sequence, supporting zero-shot forecasting.
DLinear (Zeng al., 2023) proposes linear forecasting model, enhanced by seasonal-trend decomposition or normal- ization.
7. PatchTST (Nie et al., 2022) uses Transformer encoders with patching and channel independence techniques for improved predictions.
TimesNet (Wu et al., 2023) applies convolution kernels along the time dimension, using temporal decomposition and periodical segmentation capture temporal patterns.
9. FEDformer (Zhou et al., 2022) employs sparse frequency domain representation, using frequency-enhance blocks for cross-time dependency.
10. Autoformer (Wu et al., 2021) uses series decomposition blocks and Auto-Correlation to capture cross-time dependency.
11. Stationary (Liu al., 2022) introduces stationarization and de-stationary attention mechanisms.
12. ETSFormer (Woo et al., 2022b) leverages exponential smoothing principles, including exponential smoothing and frequency attention mechanisms.
13. Informer (Zhou et al., 2021) proposes ProbSparse self attention and distillation operations.
13

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VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Table Periodicity (P) search range for the sampling frequeney. denotes the numher of sampling frequencies. For example, for data with sampling frequency of 2 minutes (2T), we have x = 2, and the possible search range of P is {1400/2, 1000/22 1] = {720, 5040, 1).
Sampling Frequency
Possible Seasonalities
Possible P
Second (S)
hour
/2600/s, 1}
Minute (T)
I day I week
[140022 10030/ 1}
Hour (II)
1 day week
22/a, 168/2,
Day (D)
I week, month, or year
7/2, 30/ c, 366/
Week (W)
year or month
52/az 4/2, 1
Month (M)
year, months, or 3 months
{12/s, 9/2, 9/2, 1/
Business Day (B)
week
{1/r,1]
Quarter
year 6 months
(/,, {1}
Others
Table 6. Final P used each dataset in our experiment.
Frequency
Datasets
H
ETThl
ETTh2
Electricity
Traffic
W
52
Illness
Long-Term TSF
15T
96
ETTml
ETTm2
10T
144
Weather
D
M4 Daily
COVID Deaths
W
NNS Weekly
M
FRED-MD
Q
M3 Other
M
Monthly
Monthly
CIF 2016 (6)
W
4
M4 Weekly
Traffic Weekly
Q
Tourism Quarterly
Monash
M
6
CIF 2016 (12)
Car Parts
D
Bitcoin
Vehicle Trips
Weather
NN5 Daily
D
US Births
Saugeen Day
Temperature Rain
M
12
Tourism Monthly
Hospital
MI Monthly
H
M4 Hourly
KDD cup
Pedestrian Counts
H
24
Traffic Hourly
Rideshare
D
Sunspot
0.5H
336
Aus. Elec. Demand
Table 7. Comparison of setting P = 1 for VISIONTS.
VISIONTS
P p=1
MSE
MAE
MSE
MAE
ETThl
0.390
0.414
0.840
0.628
ETTh2
0.333
0.375
0.424
0.445
ETTml
0.374
0.372
0.660
0.533
ETTm2
0.282
0.321
0.312
0.363
Average
0.344
0.370
0.559
0.492
For the long-term TSF benchmark, we include TS-based foundation model results from their original papers, Text-based model results from Tan et al. (2024), and other baseline results from Zhou et al. (2023). For the Monash and PF benchmark, we include results from Woo al. (2024).
Environment All experiments are conducted using Fime-Series-Libaan
com thuml/
Time- Seriesrrss esbbirrrrrrrr and GluonTS library (Alexandrov et al., 2020) on an NVIDIA A800 GPU.
14

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better than VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
A.2. Periodicity selection
We first determine range of period lengths based the sampling frequency of the data, shown in Table 5. This frequency- based strategy is also employed by Alexandrov al. (2020) while extend the search range for tuning. We select the optimal P from this range the validation set. The final P used in experiments summarized in Table 6.
To demonstrate the influence of P and the effectiveness of our periodicity selection strategy, we set P = 1 and compare the results with the above strategy. Table 7 shows that such strategy (denoted as VISIONTS) significantly outperforms the naive strategy that sets P =
B. Zero-Shot Forecasting
B.I. Hyperparameters
Table 8. Hyperparameters for VISIONTS used our zero-shot forecasting (Long-term TSF),
ETThi
ETTh2 ETTml ETTm2
Weather Electricity
Normalization constant
0.4
0.4
0.4
0.4
0.4
0.4
Alignment constant c
0.4
0.4
0.4
0.4
0.4
0.4
Context length
2880
1728
2304
4032
4032
2880
We conduct hyperparameter tuning on validation sets to determine the optimal context length L. Final used hyperparameters are summarized in Table 8.
B.2. Full forecasting results of the long-term TSF benchmark
Table 9 shows the full results zero-shot/few-shot long-term forecasting performance. VISIONTS achieves the best results most cases (32 out of 62), outperforming MOIRAlRase (10 out of 62) and MOIRAlI.are (8 out 62).
B.3. Comparison of FM and LLMTime
Due to the step-by-step output of the decoder architecture, the efficiency of TimesEM (Das et al., 2024) and LLMTime (Gruver et al., 2023) are relatively slower. Thus, Das al. (2024) only reported results for the last test window of the original split. We compared VISIONTS with their results under the same setting, as shown in Table 10. VISIONTS outperforms TimesFM and LLMTime in terms of MAE, indicating that image-based TSF models on par with TS-based and text-based models.
B.4. Comparison of traditional methods
In addition deep learning models, we also compare traditional methods, including ARIMA, ETS, and methods that require periodicity as VISIONTS: Seasonal Naïve (repeating the last period) and Seasonal Avg (similar to Scasonal Naïve but repeating the average of all periods in the look-back window). Due to the high computational cost of ARIMA and ETS, we only compare them on the small-scale benchmarks, i.e., four ETT datasets. Table 12 shows that VISIONTS also achieves the best performance.
B.5. Comparison of concurrent works
We compare our work with other concurrent TSF methods. Table 13 presents the comparison from Time-MoE (Shi et al., 2024) and TTM (Ekambaram et al., 2024), and Table 14 shows the comparison with CALF (Liu et al., 2025), which is the existing SOTA LLMs-based time series forecasting work. These findings highlight the promising potential of vision models TSF scenarios.
B.6. Full forecasting results of the Monash TSF benchmark
Setup Table lists the sampling frequency and the selected period P for each dataset. Datasets with P indicate no significant periodicity, where we use context length of T. 300. For other datasets with P 1, we select a longer context
15

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VISIONTS:
Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Table 9. Full results of Table I: Zero-shot or few-shot results on the long-term term TSF benchmark. Bold: the best result.
Zero-Sbot
Few-Shot (10% Downstream Dataset)
Pretrain
Images
Time-sories
Text
No Pretrain
Method
VISION TIME
MOIRAS
MOIRAIe
MOIRAL
TimeLLM
GPT4TS
DLinear
Pat
TimesNet
Autoformer
Informer
Metric
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAB
MSE
MAB
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE MAE
MSE MAE
96
0.353
0.383
0.375
0.402
0.384
0,402
0.380 0.398
0.448 0,460
0.458
0.456
0.492 0,495
0.516
0.485
0.861
0.628
0.613 0.552
1.179 0.792
ETTm2 ETTml E7752 E77h1
192
0.392
0.410
0.399
0.419
0.425
0.429
0.440 0.434
0.484
0.483
0.570 0.516
0.565
0.538
0.598
0.524
0.797
0.593
0.722 0.598
1.199 0.806
336
0.407
0.423
0.412
0.429
0.456
0,450
0.514 0.474
0.589
0,540
0.608
0.535
0,721
0.622
0.657
0.550
0.941
0.548
0.750
0.619
1,202 0.11
720
0.406
0.441
0,413
0.444
0.470
0,473
0.205
0.568
0.700
0.604
0.725
0.591
0.986
0.743
0.762
0.610
0.877
0.541
0.721
0.616
1.217 0.825
0.390
0.414
0.400
0.424
0.434
0.439
0.510
0.469
0.556
0.522
0.590
0,525
0.691
0.600
0.633
0.542
0.869
0.628
0.702
0.596
1,199 0.809
96
0.271
0.328
0.281
0.334
0.277
0.327
0.287
0.325
0:275
0.326
0.331
0.374
0.357
0,411
0.353
0.389
0.378
0.409
0.413
0:451
3.837
1.508
192
0.328
0.367
0.340
0.373
0.340
0.374
0.347
0.367
0.374
0,573
0,402
0.411
0.569
0.519
0,403
0.414
0.490
0.467
0.474
0.477
3,856
1.513
336
0.345
0.381
0,362
0.393
0.371
0.401
0.377
0.393
0.406
0.129
0,406
0.433
0.671
0.572
0.426
0.441
0.537
0.494
0.547
0.543
3,952
1.526
720
0.388
0.422
0,380
0,416
0.394
0.466
0.404
0.421
0.427 0,449
0,449
0,464
0.824
0.648
0.477
0.480
0.510
0.491
0.516
0.523
3,842
1,503
avg
0.333
0.375
0.379 0,341
0,346
0.382
0.354
0.377
0.370
0.394
0.397
0.421
0,605
0.538
0.415
0.431
0.479
0,465
0,488
0.499
3,872
1.513
96
0.341
0.347
0,404
0.383
0.335
0.360
0.353
0,363
0.346 0,388
0,390
0,404
0.352
0.392
0,410
0.419
0.583
0.501
0.774
0.614
1,162
0.785
192
0.360
0.360
0.435
0.402
0.366
0.379
0.376
0.380
0.373
0.416
0.429
0.423
0.382
0.412
0.437
0.434
0.630
0.528
0.754
0.592
1.172 0.793
336
0_377
0.374
0.462
0,416
0.391
0.394
0.399
0.395
0.413 0.426
0,469
0.439
0.419
0,434
0.476
0.454
0.725
0.568
0.869
0.677
1,227
720
0.416
0.405
0,490
0.437
0.434
0.419
0.432
0.417
0.485
0.476
0.569
0.498
0.490
0.477
0.681
0.556
0.769
0,549
0,810
0.630
1.207
0.797
avg
0.374
0.372
0.448
0.110
0,382
0,388
0,390
0.389
0.404
0.427
0.464
0.441
0.411
0,129
0.501
0.166
0.677
0.537
0.802
0.628
1.192
0.821
96
0.228
0.282
0,205
0.282
0. 1.195
0.269
0.189
0.260
0.177
0.261
0.188
0.209
0.213
0.303
0.191
0,274
0.212
0.288
0.352
0.454
3,203
1.407
192
0,262
0,305
0.261
0.318
0,247
0.303
0.247
0.300
0.241
0,314
0,251
0,309
0.278
0,345
0.252
0.317
0.270
0.323
0.694
0.691
3.112
1.387
336
0.293
0.128
0,319
0.355
0.291
0.333
0.295
0.334
0.274 0.327
0.307
0.346
0.338 0.385
0.306
0.353
0.323
0.353
2.40N
1.407
3,255
1,421
720
0.343
0.370
0.415
0,410
0.355
0.377
0.572
0.386
0.417
0,390
0.426
0.417
0.436
0,440
0.433
0.427
0.474
0,449
19913
1,166
3,909
1.543
ovg
0.282
0.321
0.300
0.341
0.272
0.321
0.276
0.320
0.277
0.323
0.293
0.335
0.316
0.368
0.296
0.343
0.320
0.353
1.342
0.930
3,370
1.400
Electriitt
96
0.177
0.266
0.205
0,299
0.158
0.248
0.152
0.242
0,139
0.241
0.139
0.237
0.
0.253
0.140
0.238
0.299
0.373
0.261
0.348
1,259
0.199
192
0.188
0.277
0,220
0,310
0.174
0.263
0.171
0.259
0.151
0.248
0.156
0,252
164
0.264
0.160
0,255
0.305
0.379
0.338
0.406
1.160
0,773
336
0.207
0.296
0.236
0.323
0.191
0.278
0.192
0.278
0.169
0.270
0,175
0,270
0.181 0,282
0.180
0.27%
0.319
0.391
0,410
0.474
1,157
0.872
720
0.256
0.337
0,270
0.347
0.229
0.307
0.236
0.313
0.240
0.322
0.233
0.317
0.223
0.321
0.241
0.323
0.369
0,426
0,715
0.685
1,203 0.898
ing
0.207
0.294
0.233
0.320
0.188
0.274
0.188
0.273
0.175
0.270
0.269 0,176
0.180
0,280
0.180
0.273
0.323
0.392
0,431
0.478
1.195
0.891
96
0.220
0.257
0.173
0,212
0,167
0.203
0.177
0.208
0.161
0,210
0.163
0.215
0.171
0.224
0.165
0,215
0.184
0,230
0.221
0.297
0.374
0.401
W Worther
0.275
0,216
0,250
0.217)
0:254
012263
0.210
0.257
0.345
0.270
0:552
336
0.280
0.299
0,260
0.282
0.256
0.276
0.277
0.292
0:261
0.302
0.256
0,292
0.258
0.299
0.259
0.297
0.305
0.321
0.320
0.351
0.724
0.541
0.337
0.320
0.346
0,332
0.346
0:381
0.390
0.739
0.269
0.292
0.242
0.267
0,238
0.261
0,260
0.275
0.234
0.273
0.238
0.275
0.241
0.283
0.242
0.279
0.279
0.301
0.300
0.342
0.597
0.495
Average
0.309
0.345
0.327
0.357
0.310
0.344
0,350
0,368
0,360
0.378
0:407
0.416
0.378
0.389
0.491
0,446
0.678
0.579
1,904
1 count
32
0
10
10
D
0
0
length of L = 1000. All datasets were tested with the hyperparameters r = c= =0.4 as we had done for the long-term TSF benchmark.
Results Table 15 presents VISIONTS' 's MAE test results, with the normalized MAE calculated by dividing each dataset's MAE by the naive forecast's MAE and aggregated using the geometric mean across datasets. We include the result of each baseline from Woo et al. (2024). Particularly, we find that VISIONTS outperforms MOIRAI some datasets with P = 1 (e.g., FRED-MD and NN5 Weekly), showing that VISIONTS can still work effectively without significant periodicity.
B.7. Impact of backbones
Table 17 compares zero-shot forecasting performance of three MAE variants (112M, 330M, and 657M), showing that the three variants are similar, but larger models show a slight decrease. Particularly, the smallest model excels in ETTh2, ETTml, ETTm2, and Weather, while the largest model excels in Electricity. Additionally, Table 16 compares VISIONTS with another visual backbone, LaMa.
B.8. Impact of the different image encoding strategies
Table 18 summarizes the impact of interpolation strategies and image orientations in the Alignment step. It shows that the smoother Bilinear and Bicubic interpolation perform similarly, both significantly better than the rougher Nearest Neighbor. This suggests that smooth resizing effectively handles time series interpolation. Moreover, image orientation has little impact on performance.
16

---

VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Table 10. MAE results of TimesFM and LLM-
Table 11. Comparison of traditional forecasting haselines in the zero-shot setting.
Time for zero-shot forecasting, on the last test window the original test split.
Method
VISIONTS
ETS
ARIMA
Seasonal Naive
Seasonal Avg
Metric
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
Method
VISIONTS
TimesFM LLM'Time
96
0.353 0.383
1.289
0.710
0.900
0.719
0.512
0.433
0.589
0.585
ETThl
96
0.45
ETThl
192
0.392
0.410
1.319
0.730
0.906
0.724
0.581
0.469
0.598
0.590
192
0.45
0.53
336
0.407
0.423
1.324
0.742
0.908
0.731
0.650
0.501
0.610
0.597
96
0.24
0,35
0.33
720
0.406
0.441
1.329
0.751
0.932
0.753
0.655
0.514
0.656
0.624
ETTh2
192
0.60
0.62
0.70
0.414 0.390
1.315
0.733
0.912
0.732
0.600
0.479
0.613
0.599
96
0.12
0.19
0.37
96
0.271
0.328
0.399
0.408
0.488
0.508
0.391
0.380
0.457
0.494
ETTml
192
0.23
0.26
0.71
ETTh2
192
0.328
0.367
0,500
0.459
0.497
0.514
0,482
0.429
0.466
0.500
96
0.19
0.24
0.29
336
0.345
0.381
0.562
0.498
0.507
0.522
0.532
0,466
0.476
0.509
ETTm2
192
0.24
0.27
0.31
720
0.388
0.422
0.558
0.506
0.572
0.557
0.525
0.474
0.542
0.548
avg
0.375 0.333
0.505
0.468
0.516 0.525
0.483
0.437
0.485
0.513
Average
0.30
0.36
0.45
96
0.341
0.347
1.204
0.659
0.702
0.568
0.423
0.387
0.369
0.399
ETTm1
192
0.360
0.360
1.251
0.685
0.704
0.570
0.463
0,406
0.374
0.402
336
0.377
0.374
1.276
0.702
0.709
0.574
0.496
0.426
0.382
0.407
720
0.416
0.405
1.311
0.724
0.713
0.580
0.574
0.464
0.394
0.416
avg
0.372 0.374
1.261
0.693
0.707
0.573
0,489
0.421
0.380
0.406
0.228
0.282
0.257
0.324
0.397
0.434
0.263
0.301
0.365
0.411
ETTm2
192
0.262
0.305
0.331
0,366
0:402
0.436
0.321
0.337
0.369
0.414
336
0.293
0.328
0.402
0.406
0.407
0.439
0.376
0.370
0.375
0.418
720
0.343
0.370
0.512
0.462
0.413
0.443
0.471
0,422
0,380
0,423
avg
0.282
0.321
0.376
0.390
0.405
0.438
0.358
0.357
0.372
0.417
Average
0.344
0.370
0.864
0.571
0.635
0.567 0
0.482
0.424 0
0.463
0.484
1st count
41
B.9. Hyperparameter analysis
Figs. 10 show the influence of three hyperparameters, r, e, and L. We report the MSE averaged on four prediction
lengths (96, 192, 336, 720).
17

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VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Table 12. Comparison of traditional zero-shot forecasting baselines.
Method
VISIONTS
ETS
ARIMA
Seasonal Naïve
Seasonal Avg
Metric
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
96
0.353
0.383
1.289
0.710
0.900
0.719
0.512
0,433
0.589
0.585
ETTh1
192
0.392
0.410
1.319
0.730
0.906
0.724
0.581
0.469
0.598
0.590
336
0.407
0.423
1.324
0.742
0.908
0.731
0.650
0.501
0.610
0.597
720
0.406
0.441
1.329
0.751
0.932
0.753
0.655
0.514
0.656
0.624
avg
0.414 0.390
1.315
0.733
0.912
0.732
0.600
0.479
0.613
0.599
96
0.271
0.328
0.399
0.408
0.488
0.508
0.391
0,380
0.457
0.494
ETT/2
192
0.328
0.367
0.500
0.459
0.497
0.514
0.482
0.429
0,466
0.500
336
0.345
0,381
0.562
0.498
0.507
0.522
0.532
0.466
0.476
0.509
720
0.388
0.422
0.558
0.506
0.572
0.557
0.525
0.474
0.542
0.548
avg
0.333
0.375
0.505
0.468
0.516
0.525
0.483
0.437
0.485
0.513
96
0.341
0.347
1.204
0.659
0.702
0.568
0.423
0.387
0.369
0.399
El
192
0.360
0.360
1.251
0.685
0.704
0.570
0.463
0.406
0.374
0.402
336
0.377
0.374
1.276
0.702
0.709
0.574
0.496
0,426
0.382
0.407
720
0.416
0.405
1.311
0.724
0.713
0.580
0.574
0.464
0.394
0.416
avg
0.372 0.374
1.261
0.693
0.707
0.573
0.489
0.421
0.380
0.406
96
0.228
0,282
0.257
0.324
0.397
0.434
0.263
0.301
0.365
0.411
ETT'2
192
0.262
0.305
0.331
0.366
0.402
0.436
0.321
0.337
0.369
0.414
336
0.293
0.328
0.402
0.406
0.407
0.439
0.376
0.370
0.375
0.418
720
0.343
0.370
0.512
0.462
0.413
0.443
0.471
0,422
0.380
0.423
avg
0.321 0.282
0.376
0.390
0.405
0.438
0.358
0.357
0.372
0.417
Average
0.344
0.370
0.864
0.571
0.635
0.567
0.482
0.424
0.463
0.484
1s count
41
Table 13. Comparison of Time-MoE and TTM in the zero-shot setting. We report the base and large model results for Time-MoF, as the ultra model weights are yet relcased. For TTM, used the official HuggingFace model for replication. The following table summarizes the performance of various zero-shot foundation models.
VISIONTS Time-MoE (base) Time-MoE (large) TTM (v1)
MSE MAE
0.390
0.400
0.394
0.398 0.421
ETTh1
0.414
0.424
0.419
MSE
0.333
0.366
0.405
0.348
ET
MAE
0.375
0.404
0.415
0.393
MSE
0.374
0.394
0.376
0.520
ETTml
MAE
0.372
0.415
0.405
0.479
MSE
0.282
0.317
0.316
0.312
ETTm2
MAE
0.321
0.365
0.361
0.348
MSE
0.207
(data leakage) (data leakage)
(data leakage)
0.201
Electricity
MAE
0.294
(data leakage)
0.293
MSE
0.269
0.265
0.270
0.234
Weather
MAE
0.292
0.297
0.300
0.266
MSE
0.309
0.335
Average
MAE
0.345
0.367
1st count
10
18

---

VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Table 14. Comparison of CALF in both zero-shot and full-shot settings.
VISIONTS (zero-shot) VISIONTS (full-shot) CALF
MSE
0.390
0.395
0.432
ETTh1
MAE
0.414
0.409
0.428
MSE
0.333
0,336
0,349
ETTh2
MAE
0.375
0.382
0.382
MSE
0.374
0.338
0.395
ETTml
MAE
0.372
0.367
0.390
MSE
0.282
0.261
0.281
ETTm2
MAE
0.321
0,319
0,321
MSE
0.207
0.156
0.175
Electricity
MAE
0.294
0.249
0,265
MSE
0.269
0.227
0.250
Weather
MAE
0.292
0,262
0.274
MSE
0.309
0.286
0.314
Average
MAE
0.345
0.331
0.343
Table 15. Full results of Fig. 5: Forecasting results (MAE) on the Monash TSF benchmark. We reported the reproduction results of LLMTime based on the GPT3.5 API from Woo et al. (2024).
VISIONTS
L.MMTime
DeepAR
Trandormer
Monthly
2567.54
3773
Monthly
737.93
877.97
$37.14
626.45
798.38
Oilarr
315.5
300.3
278.45
237.83
194.95
234.43
240.17
247.56
35
230.34
M4 Monthly
666.54
725.27
597.6
671.27
625.24
563.58
589.52
582.6
575.36
996.19
611.69
612.52
615.22
578.48
655 51
750.47
M4 Workly
434.3
515.44
999.00
$47.90
836.0
335.33
206.1 15
535 655.65
521.1
203.1
16465
538.37
551.76
277.73
3.9 45
178
M4 Daily
215.55
266.52
189.1
18083
178.27
178.86
176.6
193.25
179.7
181.92
231.36
177.91
299.99
190.44
189
201.88
Hourly
258.37
576.06
1213.06
1213.06
257.39
285.35
385.40
393.63
320.56
Quanedy
12931
1691886
18352.44
15845.1
15014.19
10475.47
06.0555
9137.12
Monthly
2960.19
6696.81
570907.34
509313.8
578996
57187507
7148186
853578.4
642421
460889
563206.57
603551.3
320018
679034.
Aus Elec. Demand
232.44
100.81
266.57
659.6
659.6
06504
370.44
1282.99 99
1005.02
247.18
241.77
258.76
302.41
21383
221.5
23145
Biucin
2.33E+ 18
L774
1.76E+15
7.74E417
1.
5,335+18
17
3.62
6.66E
1993+48
1.45E+18
1.95
1.08E+18
2.46E+18
2.61E
Pedesmian Counts
52.01
97.77
$4.85
170.5
170.T
17099
222.36
216.5
635.16
44.18
43.41
46,41
44.78
66.34
46.46
47.29
Mchicle Trips
22.06
31.45
24.45
31.42
39.58
50.76
11.11
30.05
30,07
27.24
22.61
22,83
21
26.16
34.15
25.01
KDO
42.72
42.15
4704
48.8
36.35
49.1
37.06
$4.66
Weuther
2,05
2.17
2.36
2.35
2.03
4.41
5.47
3.94
3.97
NNS Werkly
14.67
1576
15.07
16.71
1488
15.7
1494
19.34
20.54
Carpans
0.58
0.44
0.53
DA8
0.55
0.5%
0.58
0.56
0.56
0,41
0.53
0.89
0,39
0:98
0-4
0.99
FRAD-MI
189367
7804.64
2569.48
2825 67
2798. 20
$490.884
1989 07
2041142
2057.11
8921 94
2475 68
2389.57
4764 96
2557.8
2500.4
4666 04
Traffi: Houryy
6U1
0.05
0.02
0.05
0,03
0,03
0.04
0:03
0.34
0,00
0.02
0.01
0.01
0.02
0,02
0.01
Tralic: Werkly
1.14
1.15
1.17
1.19
1.12
1.13
1.17
1.14
1.22
L:13
1.17
1.15
1.18
1.11
1.2
1.42
Rideshare
6.29
1790
16.19
Deaths
137.51
124.32
353.71
201.95
158.1
Tamperatuue
6.37
9.39
8.21
7.39
6.33
5.56
5:37
7.28
3.81
5.34
Suaspot
2,81
5:07
0,11
3.93
4,93
4,93
2.57
4.59
2.57
3.83
2.27
7.90
0.77
14,47
0.17
0.13
Suugans River Flan
50.22
34.84
34.07
21.5
21.5
49.49
22.36
30.69
22.38
25.34
11.28
22.98
33.51
37.92
22.17
28.06
US Birhs
519.44
1374,99
$72.51
1152.67
1192.3
$86.93
399
419.73
526.35
$74.93
441.7
567387
424.95
422
504.4
452.87
Normalized MAE
0.729
1.041
0361
1,000
1,028
0.927
0.758
0.872
0.898
0.785
0.760
0.741
0,759
0.785
0.749
0.700
Rank
16
14
Table Comparison LaMa as the backbone. Results are averaged on four prediction lengths.
MAE
LaMa
MOIRAI suall
MOIRAILarg
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
E
0.390
0.414
0.425
0.433
0.400
0.424
0.510
0.469
ETTh2
0.333
0.375
0.376
0.408
0.341
0.379
0.354
0.377
ETTml
0.374
0.372
0.400
0.391
0.448
0.410
0.390
0.389
ETTm2
0.282
0.321
0.294
0.337
0,300
0.341
0.276
0.320
Average
0.344
0.370
0.374
0.392
0.372
0.388
0.382
0.388
19

---

VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Table 17. Full results Table zero-shot forecasting results of different MAB variants. Bold: best results among three variants, We also
include the results from MOIRAI for reference.
MAE (Base)
(Large)
MAE (Huge)
MOIRAI (Small)
MOIRAI (Base)
MOIRAL (Huge)
Method
112M
330M
657M
14M
91M
311M
Metric
MSE
MAR
MSE
MAR
MSE
MAE
MSE
MAE
MSE
MAF
MSE
MAE
96
0.353
0.383
0.346
0.382
0.362
0.384
0.375
0.402
0.384
0.402
0.380
0.398
B7712 ETTML
192
0.392
0.410
0.379
0.406
0.407
0.414
0.399
0.419
0.425
0.429
0.440
0.434
336
0.407
0.423
0.391
0.416
0.399
0.419
0.412
0.429
0.456
0.450
0.514
0.474
720
0.406
0.397
0.433
0.433
0.413
0.444
0.470
0.473
0.705
0.568
avg
0.390
0.414
0.409 0.378
0.391
0.412
0.400
0.424
0.434
0.439
0.510
0.469
0.271
0.328
0.286
0.333
0.334
0.277
0.327
0.287
0.325
192
0.328
0.367
0.346
0.375
0.337
0.369
0.340
0.373
0.340
0.374
0.347
0.367
336
0.345
0.381
0.356
0.387
0.357
0.388
0.362
0.393
0.371
0.401
0.377
0.393
720
0.388
0.422
0.371
0.409
0.379
0.412
0.380
0.416
0.394
0.426
0.404
0.421
avg
0.333
0.375
0.340
0.377
0.339
0.375
0.341
0.379
0.346
0.382
0.354
0.377
96
0.341
0.347
0.344
0.349
0.352
0.351
0.404
0,383
0.335
0.360
0.353
0.363
m
192
0.360
0.360
0.365
0.363
0.360
0.367
0.435
0.402
0.366
0.379
0.376
0.380
336
0.377
0.374
0.381
0.376
0.381
0,383
0.462
0.416
0.391
0.394
0.399
0.395
720
0.416
0.405
0.429
0.411
0.440
0.412
0.490
0.437
0.434
0.419
0.432
0.417
avg
0.372 0.374
0.379
0.375
0.383
0.378
0.448
0.410
0.382
0.388
0.390
0.389
96
0.228
0.282
0.225
0.282
0,229
0.282
0.205
0.282
0.195
0.269
0,189
0.260
ETTm2
192
0.262
0.305
0.262
0.305
0.265
0.306
0.261
0.318
0.247
0.303
0.247
0.300
336
0.293
0.328
0.299
0.331
0.286
0.324
0.319
0.355
0.291
0.333
0.295
0.334
720
0.343
0.370
0.358
0.377
0.355
0.374
0.415
0.410
0.355
0.377
0.372
avg
0,282
0.321
0.324 0,286
0.284
0.322
0.300
0.341
0.272
0.321
0.276
0.320
Electricity
0.177
0.266
0.177
0.170
0.259
0.205
0.299
0.158
0.248
0.152
0.242
192
0.188
0.277
0.192
0.283
0.182
0.273
0.220
0,310
0.174
0.263
0.171
0.259
336
0.207
0.296
0.213
0.303
0.207
0.295
0.236
0.323
0.191
0,278
0.192
0.278
720
0.256
0.337
0.337 0.256
0.250
0.333
0.270
0.347
0.229
0.307
0.236
0.313
avg
0.207
0.294
0.209
0.298
0.202
0.290
0,233
0.320
0.188
0.274
0.188
0.273
96
0.220
0.257
0.222
0.257
0.235
0.265
0.173
0.212
0.167
0,203
0.177
0.208
W Weather
192
0.244
0.275
0.246
0.275
0.276
0.288
0.216
0.250
0.209
0.241
0.219
0.249
336
0.280
0.299
0.283
0,301
0.304
0.309
0.260
0.282
0.256
0.276
0.277
720
0,330
0.337
0.338
0.343
0.351
0.350
0.320
0.322
0.321
0.323
0.365
0.350
0.269
0.292
0.272
0.294
0.292
0.303
0.242
0.267
0.238
0.261
0.260
0.275
Average
0,309
0.345 38
0.311
0.346 17
0.315
0.347 17
0.327
0.357
0.310
0.344
0.329
0.350
1s count
20

---

VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Table 18. Impact of resampling filters and image orientations.
Interpolation strategies in resampling
Image orientation
Method
Bilinear
Bicubie
Nearest Neighbor
Method
Horizontal flip
Vertical flip
Metric
MSE
MAE
MSE
MAB
MSE
MAE
Metric
MSE
MAE
MSE
MAE
MSE
MAE
96
0.353 0.383
0.351
0.383
0,426
0.424
96
0.353
0.383
0.348
0.379
0.355 0.385
ETTh1
192
0.392
0.410
0.392
0.409
0.450
0.443
192
0.392
0.410
0.386
0.404
0.394
0,411
336
0.407
0.423
0.407
0.422
0.451
0.450
336
0.407
0.423
0.401
0.416
0.408
0.423
720
0.406
0.441
0.405
0.440
0.454
0.470
720
0.406
0.441
0.399
0.430
0.406
0.442
avg
0.390
0.414
0.389
0.414
0.445
0.446
avg
0.390
0.414
0.384
0.407
0.391 0,415
96
0.271
0.328
0.274
0.329
0.298
0.349
96
0.271
0.328
0.274
0.329
0.274
0.330
ETTh2
192
0.328
0.367
0.330
0.367
0.343
0.380
ETTh2
192
0.328
0.367
0.331
0.370
0.330
0.367
336
0.345
0.381
0.345
0.380
0.373
0.401
336
0.345
0.381
0.347
0.386
0.345
0.381
720
0.388
0.422
0.386
0.419
0.404
0.431
720
0.388
0.422
0.376
0.416
0.388
0,422
avg
0.375 0.333
0.334
0.374
0.354
0.390
avg
0.333
0.375
0.332
0.375
0.375 0.334
0.341
0.347
0.366
0.354
0.399
0.374
96
0.341
0.347
0.345
0.348
0.342
0.347
ETTml
192
0.360
0.360
0.383
0.367
0.397
0.376
ETTml
192
0.360
0.360
0.364
0.362
0.360
0.360
336
0.377
0.374
0.396
0.381
0.386
0.380
336
0.377
0.374
0.378
0.375
0.377
0.374
720
0.416
0.405
0.429
0.409
0.417
0.409
720
0.416
0.405
0.419
0.408
0.417
0.405
avg
0.372 0.374
0.393
0.378
0.400
0.384
avg
0.374
0.372
0.376
0.373
0.372 0.374
96
0.228
0.282
0.246
0.296
0.264
0.326
96
0.228
0.282
0.230
0.286
0.228
0.283
mT m2
192
0.262
0.305
0.273
0.313
0,273
0.328
m2
192
0.262
0.305
0.264
0.308
0.262 0.305
336
0.293
0.328
0.303
0.334
0.297
0.343
336
0.293
0.328
0.298
0.332
0,293 0.328
720
0.343
0.370
0.343
0.370
0.334
0.369
720
0.343
0.370
0.350
0.373
0.343 0.369
avg
0.282
0.321
0.291
0.328
0.292
0,341
avg
0.282
0.321
0.285
0.325
0.282 0.321
Average
0.370 0.344
0.352
0.373
0.373
0.391
Average
0.344
0.370
0.344
0.370
0.345
0.371
count
30
18
st count
21
21

---

VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
E77h1
ETT12
ET7ml
0.46
0.44
0.42
0.40
0.40
0.34
0.2
0.6
0.0
1.0
0.2
0.6
0.8
1.0
02
0.4
0.6
1.0
(a) ETTh1
(b) ETTh2
(c) ETTml
ETTm2
Electricity
Weather
0.31
0.32
0.30
0.31
0.23
0.30
0.22
0.28
0.21
0.2
0.6
0.6
1.0
(d) ETTm2
Electricity
(f) Weather
Figure 8. MSE (Y-axis) performance of different normalization constants, (X-axis).
ETTh1
ETTh2
ETTm1
0.43
0.40
0.42
0.39
0.38
(a) ETThl
(b) ETTh2
(c) ETTml
ETTm2
Electricity
Weather
0.30
0.24
0.32
0.29
0.23
0.28
0.27
0.28
0.21
0.26
0.2
04
0.6
0.8
1.0
0.2
0.4
0.6
0.8
1.0
0.2
0.4
0.6
0.8
1.0
(d) ETTm2
(e) Electricity
(l) Weather
Figure MSE (Y-axis) performance of different alignment constants c (X-axis).
0.42
0.41
0.40
0.39
0.40
0.38
1000 2000 3000 4000
1000
2000 3000 4000
1000
2000
3000 4000
(a) ETTh1
(b) ETTh2
(c) ETTml
ETTm2
Electricity
Weather
0.24
0.30
0.23
0.30
0.29
0.28
0.20
3000
1000
4000
1000
(d) ETTm2
(e) Electricity
(f) Weather
Figure 10. MSE (Y-axis) performance of different context lengths L (X-axis).
22

---

VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
C. Full-Shot Forecasting
C.I. Training details
Table 19. Final hyperparameters for VISIONTS used in our full-shot forecasting.
ETTh1
ETTh2
ETTml
ETTm2
Illness
Weather
Traffic
Electricity
Normalization constant
0.4
0.4
0.4
0.4
1.0
1.0
0.4
0.4
Aligament constant c
0.4
0.4
0.4
0,4
0.4
0.7
0.4
0.4
Context length L
1152
1152
2304
1152
104
576
1152
1152
Based on the principle of channel independence (Nie et al., 2022; Han et al., 2024), we treat the variables of each time series as individual data samples. We use an Adam optimizer with a learning rate 0.0001 and a batch size 256 to fine-tune MAE. All experiments are repeated three times. The training epoch is one for all the datasets except Illness, for which we train MAE for 100 epochs with an early stop due to the limited training dataset scale. We conduct tuning on validation sets for the three hyperparameters, r, c, and L. The final hyperparameters used are summarized in Table 19.
C.2. Full results and standard deviations
Table 20. Standard deviations of full-shot experiments.
Method
VISIONTS
Time-
GPT4TS
Metric
MSE
MAE
MSE
MAE
MSE
MAE
96
0.347 + 0.002 0,376 : 0.000
0.376
0.003
0,402 + 0.002
0.570 =: 0.003 0.389 + 0.001
0.001
0,400
0.407
0.003
0,421
0,412 0.003
0.001
336
0.407
+ 0.001
0.415
: 0.001
0.450
0.004
0.438
+: 0.001
0.448
0.003
0.431 :: 0.001
0.001
0.457
0.001
0.411 0.003
0.000
96
0.269 + 0.003 0,328 = 0.002
0,286 1 0.003
0,346
1: 0.002
0,280 2: 0.001
0.335 : 0.001
0.374
0.003
0.391
0.348
336
0.351 + 0.002 0,395 = 0.002
0,390
0.003
0.414 1 0.002
0,380
0.002 0.405 : 0.001
0.430
0.002
0.405
0.003
0,406
96
0.281 + 0.001 0.322 = 0.001
0.291
0.001
0.341 + 0.001
0.300 + 0.001
0.340 + 0.000
ETiml
0.006
0,353
0.341
0.001
0.369
0.343 0.001
336
0.356 + 0.003
0,379
= 0.002
0,359
0.002
0.379 1: 0.001
0,376 2: 0.001
0.386 1: 0.000
0.001
0.413
0.001
0.001
96
0,169
0.003
0,256 0.002
0.162 + 0.901
0.248 + 0.001
0.163
0.001
0.249 +: 0.001
ETTm2
0.225
0.003
0..94-
0,235 0.002
0.304
0.222
0.001
0.291 +
336
0,278
0.002
0,334 0.001
0,280 0,002
0,329 + 0.001
0,273
0.001
0.327 + 0.001
720
0.372
0.002
0.392
0.002
0.002 0.366
0.357
0.001
+
96
0.142 + 0,000
0.192
0.001
0,155
0,001
0.199 1 0.001
0.148
0,001
0,185 + 0.000
Weather
192
0.191
0.000
01.33
0.000
0,223
0.001
0.261
+ 0.001
0.192 0.001
0.230 + 0.900
336
0.246
0,003
0,282
0.001
0,251
0.001
0.279 1 0.001
0,246
0.001
0.273 + 0.000
720
0.378
0.00
0.337
0.001
0.345
0.001
0.342 + 0.001
0.320
0.001
0.328 + 0.900
96
0.344 1: 0,001
0.236 :: 0.000
0.392
0,001
0.267 1 0.000
0.396
0.001
0,264 1: 0.000
Traffic
192
0.372
1: 0.001
0,249 1: 0.001
0.409
0.001
0.271 +: 0.000
0,412
0.001
0.268 + 0.000
336
0.383
0,001 0.257
:: 0.001
0,434
0,001
0.296 1 0.000
0.421
0.001
0.273 1: 0.000
720
0,422 1: 0.001 0,280
d: 0.000
0.451
0.001
0.291 4: 0.000
0.455
0.001
0.291 + 0.000
Electricit
96
0.126
0.000
0.218 :: 0.000
0,137
0,000
0.233 4: 0.000
0.141
0.000
0.239 +: 0.000
192
0.146
1 0.001
0.239
+ 0.001
0.152
0.000
0.247 + 0.000
0.15%
0.000
0.253 + 0.000
336
0.161
0.001
0.255
:: 0.001
0,169
0.000
0.267 : 0.000
0.172
0.000
0.266 + 0.000
720
0.193
1 0.000
0,286
1: 0.000
0.200
0.000
0.250 + 0.000
0.207
0.000
0.293 + 0.000
1 count
Table 21 shows the full results of the full-shot experiments. We also report the standard deviations of our full-shot experiments computed on three runs in Table 20, including the results of Time-LLM and GPT4TS from Tan et al. (2024) for reference.
C.3. Ablation study and fine-tuning strategy comparison
We compare the following ablation variants to verify the role of the visual model (VM), similar to Tan et al. (2024).
w/o VM removes all the transformer blocks in encoders and decoders.
23

---

VISIONTS: Visual Masked Autoencoders Are Free-Lunchh Zero-Shot Time Series Forecasters
Table 21. Full results of Table 4: Full-shot forecasting performance on the long-term TSF benchmark. VISIONTS is fine-tuned only a single epoch on each dataset except for Illness.
Pretruin
Images
Text
No Pretrain
Method
VISIONTS
Time-LLM
GPT4TS
DLinear
PatchTST
TimesNet
FEDformer
Antoformer
Stationary
STSromeer
Informer
Metric
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
96
0.347
0.376
0.376
0,402
0.370
0.389
0.375
0.399
0.370
0.399
0.384
0.402
0.376
0.419
0.449
0,459
0.513
0.491
0.494
0.479
0.865
0.713
E7T12 ETTM
192
01,355
0.400
0.407 0.421
0.412
0.413
0.405
0.416
0.413
0.421
0.436
0.429
0.420
0.448
0,500
0.482
0.534 0.504
0.538 0.504
1.008 0.792
336
0.407
0.415
0.430
0.438
0.448
0.431
0.439
0.443
0.422
0.436
0.491
0.469
0.459
0.465
0.521
0.496
0.588
0.535
0.574
0.521
1.107
0.809
720
0.439
0.443
0.457
0,468
0.441
0.449
0.472
0.490
0.447
0,466
0.521
0.500
0.506
0.507
0.514
0.512
0.643
0.616
0.562
0.535
1.18]
0.865
agg
0.395
0.409
0.418
0.432
0.418
0.421
0.423
0.437
0.413
0.431
0.458
0:450
0.440
0.460
0.496
0.487
0.570
0.537
0.542 0.510
1.040
0.795
96
0.269
0.328
0.286
0,346
0,280
0.335
0.289
0.353
0.274
0.336
0.340
0.374
0.358
0.397
0,346
0.388
0.476
0.458
0.340
0.391
3.755
1.525
192
0.332
0.374
0.361
0,391
0.348
0.380
0.383
0.418
0.339
0.379
0,402
0.414
0.429
0.439
0.456
0.452
0,512
0.493
0.430
0.439
5.602
1,931
336
0.351
0.395
0.390
0,414
0,350
0.405
0.448
0.465
0.329
0,380
0.452
0.452
0.496
0.487
0.482
0,486
0.552
0.551
0,485
0.479
4.721
1.835
720
0.390
0.430
0,405
0,134
0.406
0.436
0.605
0.551
0.379
0.422
0,462
0.468
0.463
0,474
0,515
0.511
0.562
0.560
0.500
0,497
3,647
1.625
aag
0.336
0.382
0.361
0.396
0.354
0.389
0.431
0.447
0.330
0.379
0.414
0.427
0.437
0.449
0.450
0.459
0.526
0.516
0.439
0.452
4,431
1.729
96
0.281
0.322
0.291
0,341
0.300
0.340
0.299
0.343
0,290
0.342
0.338
0.375
0.379
0.419
0.505
0.475
0.386
0.398
0.375
0.398
0.672
0.571
F7Tm2 E77ml
192
0.322
0.353
0.341
0,369
0.343
0.368
0.335
0.365
0,332
0,369
0.374
0.387
0.426
0.44!
0,553
0.496
0.459
0.444
0.408
0,410
0.795 0.669
336
0.356
0.379
0.359
0.379
0.376
0.386
0.369
0.386
0,366
0.392
0.410
0.411
0.445
0.459
0.621
0.537
0.495
0.464
0.435
0.428
1.212
0.871
720
0.391
0.413
0.433
0,419
0.431
0,416
0.425
0.421
0,416
0.420
0.478
0,450
0.543
0,490
0.671
0.561
0.585
0.516
0.499
0,462
1,166 0.823
aeg
0.338
0.367
0.356
0.377
0,363
0.378
0.379 0.357
0.351
0.381
0,400
0.406
0.448
0.452
0.588
0.517
0.481
0:456
0.429
0.425
0.961
0.734
96
0.169
0.256
0.162
0.248
0.163
0.249
0.167
0.269
0.165
0.255
0.187
0.267
0.203
0.287
0.255
0.339
0.192
0.274
189
0.280
0,365
0.453
192
0.225
0.294
0.235
0,304
0,222
0.291
0.224
0.303
0.220
0,292
0,249
0.309
0.269
0.328
0.281
0,340
0,280
0.339
0:253
0.319
0,533
0,563
336
0.278
0.334
0.280
0.329
0.273
0.327
0.281
0.342
0.274
0.329
0,321
0.351
0.325
0.366
0.339
0.372
0.334
0.361
0.314
0.357
1.363
0.887
720
0.372
0.392
0,366
0,382
0.357
0,376
0.397
0.421
0,362
0.385
0.408
0.403
0.421
0.415
0.433
0.432
0.417
0.413
0..41
0,413
3,379
1.338
aeg
0.261
0.319
0.261
0.316
0.254
0.311
0.267
0.334
0.255
0.315
0.291
0.333
0.305
0.349
0.327
0.371
0.306
0.347
0.293
0.342
1.410
0.810
24
2.034
0.937
0,807
1.869
0.823
2.215
1.081
1.319
0.754
2.317
0.934
3,228
1,260
3,483
1,287
2.294
0.945
2.527
1.020
5.764
1.677
Iilnoxe
36
1,866
0.888
1.833
0,833
1,853
0.854
1.963
0.963
1.430
0.834
1,972
0.920
2.679
1.080
3,103
1,148
1,825
0.848
2.615
1.007
4,755
1.467
48
1.784
0.870
2.269
1.012
1886
0.855
2.130
1.024
1.553
0.815
2,238
0.940
2.622
1.078
2.669
1.085
2,010
0.900
2.359
0.972
4.763
1.469
60
1.910
0.912
2.177
0,925
1,877
0.877
2.368
1.096
1,470
0,788
2.027
0.928
2.857
1,157
2.770
1.125
2.178
0.963
2.487
1,016
5,264
1.564
avg.
1.899
0.902
2,018
0.894
1.871
0.852
2. 2,169
1 1.041
1.443
0.798
2.139
0.931
2.847
1. 144
3,006
1.161
2.077
0.914
2,497
1 1.004
5.137
1.544
96
0.142
0.192
0.155
0.199
0.148
0.188
0.176
0:237
0.149
0.198
0.172
0.220
0:217
0.296
0.266
0,336
0.178
0.223
0.197
0.281
0,300
0,384
Weather
192
0.191
0.238
0.223
0,261
0,192
0,230
0.220 0.282
0,194
0,241
0,219
0.261
0.276
0.336
0,307
0,367
0,245
0.285
0.237
0,312
0,598
0,544
336
0.246
0.282
0.251
0.279
0,246
0,273
0.265
0.319
0.245
0.282
0,280
0.306
0.339
0.380
0.359
0,395
0.321
0.338
0.298
0.353
0.578
0.523
720
0.328
0.337
0,345
0,342
0.320
0,328
0.333
0.362
0,314
0,334
0,365
0,359
0.403
0.428
0,419
0.428
0,414
0.410
0.352
0,388
1.059
0,741
aeg
0.227
0.262
0.244
0.270
0.227
0.255
0.249
0.300
0.226
0,264
0.259
0.287
0.309
0.360
0.338
0.382
0.288
0.314
0.271
0.334
0.634
0.548
96
0.344
0.236
0.392
0.267
0.396
0,264
0.410
0.282
0.360
0.249
0.321
0.587
0.366
0.613
0,388
0.612
0.338
0.607
0.392
0,719
0.391
Troffic
192
0.372
0.249
0,409
0,271
0,412
0.268
0.423
0.287
0,379
0,256
0.617
0.336
0.604
0.373
0.616
0,382
0.613
0.340
0.621
0.399
0,696
0.379
336
0.383
0.257
0.434
0.296
0.421
0.273
0.436
0.296
0.392
0.264
0.629
0.336
0.621
0.383
0.622
0.337
0.618
0.328
0.622
0.396
0.777
0.420
720
0.422
0.280
0.451
0.291
0.455
0.291
0.466
0.315
0.432
0,286
0.640
0.350
0.626
0.382
0.660
0,408
0.653
0.358
0.632
0.396
0.864
0.472
avg
0.256 0,380
0.422
0,281
0.421
0,274
0.434
0.295
0
0,264
0.620
0,336
0.610
0.376
0.628
0,379
0.624
0,340
0.621
0.396
0,764
0,416
Elcefricity
96
0.126
0.218
0.137
0.233
0.141
0.239
0.140
0:237
0.129
0.222
0.168
0.272
0.193
0.308
0.201
0.317
0.169
0.273
0.187
0.304
0.274 0.368
192
0.144
0.237
0,152
0,247
0,158
0.253
0.153
0.249
0,157
0,240
0,184
0.289
0.201
0.315
0,222
0.334
0,182
0.286
0.199
0.315
0,296
0,386
336
0.162
0.256
0.169
0.267
0.172
0.266
0.169
0.267
0.163
0.259
0.198
0.300
0.214
0.329
0.231
0.338
0.200
0.304
0.212
0.329
0,300
0.394
720
0.192
0.286
0,200
0.290
0,207
0.293
0.203
0.301
0.197
0.290
0,220
0.320
0.246
0.355
0.254
0,361
0.222
0.321
0.233
0.345
0.373
0.439
avg
0.156
0.249
0.165
0.259
0.170
0.263
0. 166
0.264
0.162
0.253
0.193
0.295
0.214
0.327
0.227
0.338
0.193
0.296
0.208
0.323
0.311
0.397
1 count
46
0
VM2Attn replaces both the encoder and decoder with a self-attention layer, matching MAE structure but with random initialization.
VM2Trsf is similar to VM2Attn but replaces them with a Transformer block (i.e., a self-attention layer plus an MLP layer).
Rand-VM keeps the same architecture as the vanilla MAE, but all the weights are randomly initialized.
We also compare fine-tuning different components in MAE as follows:
All fine-tunes all the trainable weights in MAE,
LN fine-tunes only the layer normalization, which is the default setting used in our experiments.
Bias fine-tunes only the bias term of all the linear layers, proposed by Zaken et al. (2022).
ML. and Attn fine-tune only the feed-forward layer and the self-attention layer, respectively.
24

---

VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Table 22, Ablation studies (Ieft) and fine-tuning strategies (right). Results are averaged on four prediction lengths: (96, 192, 336, 720).
Ablation on Visual MAE (VM)
Ablation on trained parameters
w/o VM
VM2Attn
VM2Trsf
Rand-VM
All
LN
Bias
MLP
Attn
Freeze
MSR
0.395
0.785
0.448
0.459
0.534
MSE
0,534
0.395
0,401
0.534
0.554
0.419
ETTh
MAE
0.409
0.649
0.458
0.462
0.470
ETTh1
MAE
0,470
0.409
0.414
0.471
0,479
0.418
MSE
0.336
0.420
0.418
0,448
0,411
MSE
0.411
0.336
0.347
0.401
0.392
0.340
ETTh2
MAE
0.382
0.453
0.445
0,457
0,432
ETTh2
MAE
0.432
0.382
0.392
0.419
0.414
0.376
MSE
0.338
0.676
0,398
0.433
0.433
0.338
0.343
0.441
0,444
ETTml
MAE
0.367
0.562
0,415
0,410
0.413
ETTml
MAE
0.413
0.367
0.368
0.415
0,415
0.372
MSE
0.261
0.379
0.274
0.292
0.288
MSE
0.288
0.261
0.256
0.292
0.289
0.305
ETTm2
MAE
0.319
0.415
0.334
0.344
0.341
ETTm2
MAE
0.341
0.319
0.318
0.342
0.339
0.334
MSE
0.333
0.565
0.384
0.399
0.417
MSE
0.417
0.333
0.337
0.417
0,420
0.360
Average
MAE
0.369
0.520
0.413
0.418
0.414
Average
MAE
0.414
0.369
0.373
0.412
0,412
0.375
1 st count
10
0
1 st.
count
0
0
Freeze does not fine-tune any weight. Note that it differs from the previous zero-shot experiment, where a longer context length was used (see Table and Table 19).
The results shown in Table 22, suggesting that visual knowledge is crucial for VISIONTS and fine-tuning the layer normalization is the best.
D. Visualization
We visualized the predictions VISIONTS in the zero-shot setting, including its input and reconstructed images. We also visualized the predictions of MoIRAl and Seasonal Naïve, with their MAE metrics for comparison. Figs. 11 to 13 show examples where VISIONTS performed well, with Fig. 11 depicting where VISIONTS underperformed, as it aggressively predicted the trend
regular pattern, while Figs. 12 and 13 display
less obvious patterns. Fig. 14 illustrates despite the lack of clear patterns in the input sequence, whereas MoIRAlIaage made more conservative predictions.
25

---

VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Input Image
(b) Reconstructed Image
MM
target prediction
(c) VISIONTS (MAE = 0.312)
MM
target prediction
(d) MOIRAILARGE (MAE= 0.503)
target prediction
(c) Seasonal Naive (MAE = 0.774)
Figure Forecasting visualization on a sample from ETThl. (a-b) Input/output images of VISIONTS. (c-e) Forecasting visualization.
26

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VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Input Image
(b) Reconstructed Image
M Why MM
m M Mar Mu jaMlen ArRoom
target prediction
(c) VISIONTS (MAE = 0.157)
M Wyr My yourell
Mmm M
target prediction
(d) MOIRAILARGE =
M Why a m
mmm W M M Nwn
Man Man
target prediction
(c) Seasonal Naive (MAE = 0.235)
Figure Forecasting visualization on a sample from ETTh2, (a-b) Input/output images of VISIONTS. (c-c) Forecasting visualization.
27

---

VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Input Image
(b) Reconstructed Image
target prediction
(c) VISIONTS (MAE 0.821)
Mm Dw
mn Mm M maman
target
prediction
dd MOIRAILARGE (MAE = 1.285)
May of M TarMMrm
target prediction
(c) Seasonal Naive (MAE 1.523)
Figure Forecasting visualization on a sample from ETTh2, (a-b) Input/output images of VISIONTS. (c-e) Forecasting visualization.
28

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VISIONTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
(a) Input Image
(b) Reconstructed Image
mone I WW MMy And M Madd In oy AA
target prediction
(c) VISIONTS (MAE = 0.327)
In MA Myy 'no mom Ma Madd by lnpponatin
target prediction
(d) MOIRAILARGE 0.12)
Monhh Mand bn by foallhi
M W WW MM yoo hove Mir
target prediction
(c) Scasonal Naive (MAE 0.364)
Figure 14. Forecasting visualization on a sample from ETThI, where MOIRAI outperforms VISIONTS in terms of MAE, (a-b) Input/output images of VISIONTS. (c-e) Forecasting visualization.
29


Reviews:
[{"review_id": "Qx6a7y1syK", "reviewer": "Reviewer_Uctb", "summary": "In this paper, the authors explore a novel direction in applying foundation models to time series forecasting. Given the intrinsic similarities between natural images and time series, such as modality, origin, information density, and features, the authors introduce VisionTS, a TS forecasting model built upon the pretrained CV foundation model MAE. By leveraging segmentation, rendering, and alignment techniques, 1D time series data is transformed into 2D matrices, enabling the reconstruction and prediction of masked horizon sequences.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your invaluable response. We are delighted that you found our paper novel, well-motivated, and with sufficient experiments and insights. Below are our responses to the concerns:\n\n> W1: How does VisionTS obtain the complete temporal information of visible patches during the alignment process?\n\n- During the alignment process, the temporal information is encoded one-to-one to the *spatial information* after transformation. Specifically, the patch at the $i$-th row and $j$-th column corresponds to the time step $j\\times N+i$ (with the input window scaled to $N(N-n)$ time steps in total). During the ViT processing, each patch receives a unique 2D positional encoding, enabling the model to capture spatial information and, consequently, the corresponding temporal information.\n\n> W2: In the zero-shot experiments in Tables 1 and 9, VisionTS underperforms compared to Moirai on half of the datasets (ETTm2, Electricity, and Weather). The authors should provide a detailed discussion and analysis of the underlying reasons.\n\n\n- A possible explanation is that **Moirai's pre-training data significantly overlaps with the domains similar to Electricity and Weather (e.g., Moirai pretraining containing 60% energy and 15% climate data)**. This possibly leads to data leakage. In contrast, VisionTS, using an MAE model trained solely on ImageNet, is free from such potential leakage.\n- Additionally, this experiment is *not* to prove VisionTS is superior to Moirai; instead, we aim to show a purely visual model can achieve performance comparable to a native time series pretrained model. Maybe we can rephrase this statement as: *In the zero-shot experiments, Moirai (trained on time series) underperforms compared to VisionTS (trained on images) on half of the time series datasets*. This underscores the promising potential of vision models in TSF.\n\n> W3: Table 1 is suggested to include TTM [1] and Time-MoE [2] as baselines as well.\n\nThank you for your suggestion. We report the base and large model results for Time-MoE since the ultra model weights are unreleased. For TTM, we used the official HuggingFace model for replication. The following table summarizes the performance of various zero-shot foundation models.\n\n|                  | VisionTS (base) | Time-MoE (base) | Time-MoE (large) | TTM (v1) | Moirai (base) |\n| ---------------- | --------------- | --------------- | ---------------- | --------- | ------------- |\n| ETTh1, MSE       | **0.390**       | 0.400           | 0.394            | 0.398     | 0.434         |\n| ETTh1, MAE       | **0.414**       | 0.424           | 0.419            | 0.421     | 0.439         |\n| ETTh2, MSE       | **0.333**       | 0.366           | 0.405            | 0.348     | 0.346         |\n| ETTh2, MAE       | **0.375**       | 0.404           | 0.415            | 0.393     | 0.382         |\n| ETTm1, MSE       | **0.374**       | 0.394           | 0.376            | 0.520     | 0.382         |\n| ETTm1, MAE       | **0.372**       | 0.415           | 0.405            | 0.479     | 0.388         |\n| ETTm2, MSE       | 0.282           | 0.317           | 0.316            | 0.312     | **0.272**     |\n| ETTm2, MAE       | **0.321**       | 0.365           | 0.361            | 0.348     | **0.321**     |\n| Electricity, MSE | 0.207           | (data leakage)               | (data leakage)                 | 0.201     | **0.188**     |\n| Electricity, MAE | 0.294           | (data leakage)               | (data leakage)                 | 0.293     | **0.274**     |\n| Weather, MSE     | 0.269           | 0.265           | 0.270            | **0.234** | 0.238         |\n| Weather, MAE     | 0.292           | 0.297           | 0.300            | 0.266     | **0.261**     |\n| avg, MSE         | **0.309**       | -               | -                | 0.335     | 0.310         |\n| avg, MAE         | 0.345           | -               | -                | 0.367     | **0.344**     |\n| 1st count        | 7               | 0               | 0                | 1         | 5             |\n\n**We hope these responses can fully address your concerns. Thank you once more for your detailed feedback!**", "claims_and_evidence": "Yes. Extensive zero-shot and full-shot experiments have been conducted on the long-term TSF benchmark, the GIFT-Eval Leaderboard, and the Monash TSF Benchmark. Additionally, efficiency evaluations have been included. The results highlight the superior performance of the approach in cross-modality forecasting research.", "methods_and_evaluation_criteria": "Yes. This method maintains the same experimental settings as the previous methods in time series forecasting.", "theoretical_claims": "This paper does not contain any theoretical discussions and claims.", "experimental_designs_or_analyses": "The experimental designs are reasonable and comprehensive.", "relation_to_broader_scientific_literature": "This paper focuses on foundation model for time series forecasting, which is basic research and could be used in widely scientific researches, such as energy, sale, and finance. Since it brings a new technique, I don’t find the specific relation between the proposed method and scientific literature in other research areas.", "essential_references_not_discussed": "I think this paper have included all essential references in this research area.", "other_strengths_and_weaknesses": "Strength:\n1. The authors investigate foundation models for time series forecasting from a novel view, and provide well-founded motivations forleveragingpretrainedvisualmodelasnumericseriesforecaster.\n2.The authors conduct extensive experimental evaluations under both zero-shot and full-shot settings, andachieve promising performance.\n3. This paper is well-written, providing sufficient analysis and key insights of the methodology and experiments.\n\n\n\nWeakness:\n1. In time series forecasting tasks, it is necessary and important to monitor the temporal order of time points or patches, such as PatchTST.How does VisionTS obtain the complete temporal information of visible patches during the alignment process?\n2. In the zero-shot experiments in Tables 1 and 9, VisionTS underperforms compared to Moirai on half of the datasets (ETTm2, Electricity, and Weather). The authors should provide a detailed discussion and analysis of the underlying reasons.\n3. Zero-shotcomparisononthelong-term TSF benchmark shown in Table 1 is suggested to include TTM [1] and Time-MoE[2] as baselines as well.\n\n[1] Ekambaram, Vijay, et al.Tiny time mixers (ttms): Fast pre-trained models for enhanced zero/few-shot forecasting of multivariate time series.NeurIPS, 2024.\n[2] Shi, Xiaoming, et al. Time-moe: Billion-scale time series foundation models with mixture of experts. ICLR, 2025.", "other_comments_or_suggestions": "Please refer to the weakness.", "questions_for_authors": "Please refer to the weakness.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "reE17IDNQP", "reviewer": "Reviewer_TiNp", "summary": "In this paper the authors propose to adapt an image masked auto encoder pretrained on ImageNet for time series forecasting. They justify their choice by the similarities between the image and the time series modalities. They empirically show that the proposed method achieves superior performance compared with other state of the art baselines.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your invaluable review. We are delighted that you believe that our paper is novel and the experiment results are impressive. Below are our responses to the concerns:\n\n> W1: ablation studies for each of the new mechanisms.\n\n> Q1: An empirical study when other methods are fed with the time series of some good alignment and the results are rescaled.\n\n- If \"other methods\" refers to *zero-shot pretrained models*, we kindly note that the alignment is applicable only to visual models, as it is meant to convert 1D time series into 2D formats. To the best of our knowledge, no existing TSF foundation models accept direct 2D input, making this alignment step inapplicable to existing models.\n- If \"other methods\" refers to *models trained from scratch*, we conducted an ablation study using the same alignment but substituting the vision backbone with various models. **Table 20 (Appendix D.3) indicates these substitutions significantly hurt performance and fail to achieve zero-shot forecasting**, underscoring the vision backbone's crucial role in VisionTS. **The mentioned SparseTSF (or TimesNet using similar alignment) cannot achieve such zero-shot forecasting as well.**\n- Beyond the alignment mechanism, we also introduce a new mechanism: using smaller standard deviations ($r$) during normalization. Thanks for your suggestions and we would like to investigate its contribution. The following table summarizes Moirai's performance with different $r$ (average MSE across four ETT datasets), indicating that $r$ significantly higher or lower than 1 lead to notable degradation. The reason is that image and time series distributions differ; the former is limited by color range while the latter is not. Therefore, this mechanism is unnecessary for other TSF foundation models.\n|$r$|0.4|0.6|0.8|1.0|1.2|1.5|\n|-|-|-|-|-|-|-|\n||0.474|0.494|0.387|0.372|0.370|0.380|\n\n> Q2: An empirical study when a universal or an improper P is fed to VisionTS.\n\n- Thank you for your suggestion. We have compared the results using P=1 on the ETT datasets in Table 7 (Appendix B.2). To further validate this, we tested P=1, 7, and 24 on the Monash dataset:\n|Proper P|P=1|P=7|P=24|LLMTime|\n|-|-|-|-|-|\n|0.729|0.931|0.957|1.102|1.041|\n\nResults indicate that selecting an appropriate P based on sampling frequency is crucial for zero-shot forecasting.\n\n> Q: if considered part of the VisionTS, they undermine the zero-shot claim of the proposed method, as these practices are very lookback window-specific.\n\n- We kindly note that **existing zero-shot foundation models also have their own mechanisms to incorporate sampling frequency based on the specific lookback window**. For example, Moirai selects an appropriate patch size based on the sampling frequency (see Appendix B.1 of [1]). TimesFM [2] even includes the sampling frequency as model input. We believe that leveraging prior data characteristics (e.g., sampling frequency and periodicity) to further enhance the performance of zero-shot models is also a promising research direction.\n\n> Comment 1: consider explaining why the current GIFT-Eval leaders are not mentioned (e.g., no corresponding publications).\n\n\n- Thank you for your suggestion. As you noted, current leaders are concurrent works, some without publications (mentioned in Line 297). **However, we plan to include them in the final paper version, as discussed with `Reviewer cUEq`.** We would also like to note that there may be data leakage issues for these concurrent works. For instance, both TimesFM2 and Chronos-bolt used the M4 dataset, while TimesFM also utilized the Weather for pretraining. In contrast, visual MAE was trained on ImageNet and ensures no leakage.\n\n> Comment 2: Table 3 is misleading, as the speed up from VisionTS is due to the alignment and the reconstruction step, and it only shows up for >1k forecast length, possibly for batches that use a same P. Moirai and TimesFM are not the best decoder only reference either, e.g., TimesFM does not implement cached decoding properly.\n\n- Thank you for your suggestion. For efficiency testing, our experimental settings are consistent with Moirai (refer to [1] Table 23), using longer forecast lengths. We choose Moirai and TimesFM for comparison since they are our baselines, and we will note in our final revision that VisionTS's evaluation uses a same P. To further address your concerns, we test shorter lengths, as shown in the table below.\n\n|Context Len|100|100|100|100|200|300|400|\n|-|-|-|-|-|-|-|-|\n|Pred Len|100|200|300|400|100|100|100|\n|Moirai (base)|0.03|0.03|0.04|0.04|0.04|0.04|0.04|\n|TimesFM|0.02|0.03|0.04|0.06|0.02|0.02|0.02|\n|VisionTS|0.04|0.03|0.03|0.03|0.04|0.05|0.05|\n\nThe table shows that VisionTS's runtime is similar to these two models. \n\n**We hope these responses can fully address your concerns. Thank you again for your detailed feedback!**\n\n[1] Unified Training of Universal Time Series Forecasting Transformers\n\n[2] A decoder-only foundation model for time-series forecasting", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "N/A.", "experimental_designs_or_analyses": "No issue.", "relation_to_broader_scientific_literature": "N/A.", "essential_references_not_discussed": "N/A.", "other_strengths_and_weaknesses": "Strength:\n- The paper presents thoughtful designs and evaluation of the transfer learning capability of visual models on time series forecasting. It's a relatively novel and intuitive approach. \n- The authors device and discuss a few key designs that are proper and exclusive for this visual approach. The empirical results are impressive.\n\nWeakness:\n- The paper lacks ablation studies to help understand the contributions from each of the new mechanisms the authors introduce as compared to a time series native pretrained model. See questions.", "other_comments_or_suggestions": "Some empirical results are slightly out dated or misinterpreted:\n- Since GIFT-eval is cited, consider explaining why the current leaderboard leaders are not mentioned (e.g., no corresponding publications).\n- Table 3 is misleading, as the speed up from VisionTS is due to the alignment and the reconstruction step, and it only shows up for >1k forecast length, possibly for batches that use a same P within. Moirai and TimesFM are not the best decoder only reference either, e.g., TimesFM does not implement cached decoding properly.", "questions_for_authors": "My main questions are around the additional practices introduced in the paper, e.g., alignment and reconstruction, reshaping into 2D with the explicit hints of the periodicity, etc. These practices are relatively agnostic to the backbone foundation model, and should be inspected separately - if considered part of the VisionTS, they undermine the zero-shot claim of the proposed method, as these practices are very lookback window specific, and it's not surprising to see that proper handling of the time scale and the periodicity can good deliver models (e.g., [1]).\n\nI'd suggest the following ablation studies to help better understand the proposed method:\n1. An empirical study when other methods are fed with the time series of some good alignment and the results are rescaled.\n2. An empirical study when a unverisal or an improper P is fed to VisionTS.\n\n[1] SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters,  https://arxiv.org/abs/2405.00946", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hXAXoGmNtr", "reviewer": "Reviewer_cUEq", "summary": "The paper proposes to utilize a pre-trained vision masked autoencoder for time series forecasting. The time series data is processed channel-independent and stacked depending on the periodicity of the series. A pre-trained vision-MAE is applied, and the result is transformed back in the series space representing the forecast.\nThe authors argue that the intrinsic pattern of vision data is more similar to time series data than text, hence, pre-trained vision models might be beneficial for TSF foundation models while LLM are not.\nThe approach was evaluated on an extensive set of benchmarks and shows promising results.\n\n## update after rebuttal\nwe thank the authors for the clarifications, I updated my score already", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your positive comments on our work. We are pleased that you find our paper **novel and well-experimented, aiding in understanding the workings of TSF foundation models.** Here are our responses to your concerns:\n\n> E1: Gift-Eval is the most comprehensive benchmark of the utilized benchmarks.\n\n> E2: Recent work further highlights problems of the respective long-term benchmark. Therefore, I would suggest emphasizing and discussing the gift-eval and monash in more depth instead of mostly highlighting the long-term benchmarks.\n\n- We fully agree that LTSF has limitations. Since this ground-breaking viewpoint has recently emerged, before it is widely accepted by the research community, we believe it is still necessary to test widely-used benchmarks. As you can see, other reviewers are still interested in the LTSF datasets.\n\n- We also agree on the need to enhance presentation of solid benchmarks. **We will add the GIFT-Eval results to the right side of the teaser (Figure 1) to further emphasize them and expand the discussion of their experimental results.** Below is further discussion on the GIFT-Eval leaderboard which will be included in our final revision:\n\n> W1: Evaluation reporting might miss certain metrics and models (see Experimental Design & Analysis). I suggest the author should update the mentioned results.\n\n> For GIFT-EVAL, only the MASE metrics, but no WQL or average rank metrics are reported. \n\n- We report MASE but not CRPS (WQL) or average rank, since **these are probabilistic metrics, not point metrics** (note that the average rank is based on CRPS-sorted results). Due to limitations of the MAE model, VisionTS is not a probabilistic forecasting model, as mentioned in Section 6. Comparing the CRPS metric of point-forecasting models with probabilistic models is unfair, as the former's CRPS tends to be significantly worse. **If we only consider the point forecasting model (e.g., TTM r1/r2 and Timer) on the leaderboard, VisionTS significantly outperforms them in both MASE and CRPS.** \n\n> Some methods that appear in the evaluation (e.g. TTM and Chronos) are not cited.\n> Although the leaderboard is even linked in the paper, some models outperforming VisionTS (ttm, chronos bolt) are not reported. Although some might be concurrent work, updating the results would be beneficial.\n\n- Thank you for your reminder and suggestion. **We will cite all the referenced papers and update our results to include those concurrent works, even though some lack published papers, as noted by `Reviewer TiNp` and mentioned in Line 297**. We also highlight that TTM's \"superior results over VisionTS\" were achieved by fine-tuning on the GIFT-Eval training dataset, not as a zero-shot model. Its zero-shot capability is significantly weaker than VisionTS, as shown in the leaderboard and already referenced in Figure 4 of our paper.\n\n- We would also like to note that there may be data leakage issues for these concurrent works. For instance, both TimesFM 2.0 and Chronos-bolt used the M4 dataset, while TimesFM also utilized the Weather for pretraining. In contrast, visual MAE was trained on ImageNet, long before gift-eval, which can ensure no leakage.\n\n> W2: Approach seems to not to scale with bigger models (see results on different MAE size)\n\n- One explanation is that larger vision models tend to memorize image-related details, leading to overfitting and harmful to time series forecasting. Moirai (ICML 2024 oral) also exhibited similar behavior, where larger models perform worse on out-of-distribution data (See Table 6 in [1]), with **even more severe degradation than VisionTS**. This is understandable given the disparity between image and time series modalities. We believe future adaptations in time series domain could alleviate this issue.\n\n- Additionally, we found that larger models are not without merit. For example, MAE (large) performs well on ETTh1, and MAE (huge) shows good results on Electricity. Exploring the scenarios for different MAE sizes is a promising research direction.\n\n> Q1:  How would you use VisionTS to model time series data that exhibits multiple periodicity?\n\n- We would assess potential periodicities based on sampling frequency and select the optimal P using the validation set. For time series without clear periodicity or complex multi-periodicity, we can try P=1, which can be effective in our experiments (Appendix C.5).\n\n**We hope these responses can fully address your concerns. Thank you once more for your detailed feedback, which greatly enhances the robustness of this paper.**\n\n[1] Unified Training of Universal Time Series Forecasting Transformers", "claims_and_evidence": "The main claim is that Vision-TS, a pre-trained vision autoencoder with time series specific pre- and postprocessing, is suitable for time series forecasting. The claim is supported by an extensive set of benchmark evaluations.\nWhile I think the claim is justified, I have some concerns about the evaluation results (see below).", "methods_and_evaluation_criteria": "Yes, the paper evaluates on multiple standard benchmark datasets for time series forecasting.\nThe method itself is a creative approach that might not constitute a model that will be actually used in practice but might help to understand why and how TSF foundation models work.", "theoretical_claims": "There are no theoretical proofs or claims.", "experimental_designs_or_analyses": "In general, the experimental design is well done as the paper evaluates multiple benchmarks (individual datasets of the three benchmarks overlap, which is fine). I only have one concern and one suggestion:\n\n1) Gift-Eval is the most comprehensive benchmark of the utilized benchmarks. The results are unfortunately discussed only very briefly in Figure 7. Hence, only the MASE metrics, but no WQL or average rank metrics are reported. Although the leaderboard is even linked in the paper, some models outperforming VisionTS (ttm, chronos bolt)  are not reported. Although some might be concurrent work, updating the results would be beneficial.\n\n2) Following the argument from point (1), gift-eval and monash reposoitry are likely the much more relevant benchmarks as they are more comprehensive as the long-term benchmark. Recent work further highlights problems of the respective long-term benchmark [1,2]. Therefore, I would suggest emphasizing and discussing the gift-eval and monash in more depth instead of mostly highlighting the long-term benchmarks.\n\n[1] L. Brigato, R. Morand, K. Strømmen, M. Panagiotou, M. Schmidt, and S. Mougiakakou, ‘Position: There are no Champions in Long-Term Time Series Forecasting’, Feb. 19, 2025, arXiv: arXiv:2502.14045. doi: 10.48550/arXiv.2502.14045.\n[2] Invited Talk by Christoph Bergmeir - Fundamental limitations of foundational forecasting models: The need for multimodality and rigorous evaluation, Time Series in the Age of Large Models Workshop NeurIPS 2024", "relation_to_broader_scientific_literature": "The work is located in the field of pre-trained time series models / foundational time series models.\nMost closely related is the work that builds upon existing language models, as VisionTS also utilized a pre-trained model that is actually pre-trained on non time series data. To the best of my knowledge, VisionTS is the first model that utilizes a pre-trained vision model.", "essential_references_not_discussed": "Some methods that appear in the evaluation (e.g. TTM and Chronos) are not cited.", "other_strengths_and_weaknesses": "Strength:\n- Creative approach that can help the understanding of what drives the performance of TSF foundation models.\n- Extensive benchmark scope\n\nWeakness:\n- Evaluation reporting might miss certain metrics and models (see Experimental Design & Analysis). I suggest the author should update the mentioned results. I especially want to note that regardless of weather, VisionTS is still the best-performing model; this would improve the paper. The novelty of the idea (which helps to further understand TSF foundation models)  with a thorough and sound evaluation is more important than outperforming SOTA. If the evaluation results are updated accordingly, I would consider increasing my score towards acceptance.\n- Approach seems to not to scale with bigger models (see results on different MAE size)", "other_comments_or_suggestions": "-", "questions_for_authors": "- As far as I understand, VisionTS relies on a specific periodicity that is used for segmentation. How would you use VisionTS to model time series data that exhibits multiple periodicity?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ULDsgx8AzW", "reviewer": "Reviewer_UjXP", "summary": "This paper propose the VisionTS, which uses the strong pre-trained ability in the vision model to help the time series modality. The core idea is the inherent similarity between the image and the time series such as trend, seasonality, and so on. To align the input of time series into image, the author first convert the time series into a 2D gray scale image followed by the MAE for prediction. The experiments are solid and the performance of the proposed visionTS is noteworthy.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your encouraging response. **We are delighted that you find our paper novel, with solid experiments and the performance is noteworthy.** Below are our responses:\n\n> Claims And Evidence: However, it is not very convincing to me since the domain gap between these two modalities. Further empirical evidence may be needed to justify why the pre-trained MAE can be used even in a zero-shot way to perform time series forecasting.\n\n- As you noted, we are the first to leverage a pre-trained vision model for zero-shot forecasting. **We understand that ground-breaking ideas often require time to gain community endorsement.** As an initial exploration, we've conducted extensive experiments to validate VisionTS's effectiveness. To our knowledge, our evaluation benchmark is the largest among existing TSF foundation models.\n\n- To explore the domain gap between the modalities further, we visualize their similarities in Fig 7. We find notable heterogeneity within time-series data across domains, with images potentially \"bridging\" these isolated time-series representations, which might explain why VisionTS outperforms some cross-domain TSF models.\n\n- We welcome any suggestions that could further strengthen the persuasiveness!\n\n> Essential References Not Discussed: The experimental comparison lacks one highly related work CALF [CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning, AAAI2025], which is the existing SoTA LLMs-based time series forecasting works. How is the performance of the proposed VisionTS compared with CALF?\n\n- We compare the full-shot results reported by CALF with VisionTS's zero-shot results below:\n\n\n\n|                  | VisionTS (zero-shot) | CALF (full-shot) |\n| ---------------- | -------------------- | ---------------- |\n| ETTh1, MSE       | **0.390**            | 0.432            |\n| ETTh1, MAE       | **0.414**            | 0.428            |\n| ETTh2, MSE       | **0.333**            | 0.349            |\n| ETTh2, MAE       | **0.375**            | 0.382            |\n| ETTm1, MSE       | **0.374**            | 0.395            |\n| ETTm1, MAE       | **0.372**            | 0.390            |\n| ETTm2, MSE       | 0.282                | **0.281**        |\n| ETTm2, MAE       | **0.321**            | **0.321**        |\n| Electricity, MSE | 0.207                | **0.175**        |\n| Electricity, MAE | 0.294                | **0.265**        |\n| Weather, MSE     | 0.269                | **0.250**        |\n| Weather, MAE     | 0.292                | **0.274**        |\n\n- We can observe that VisionTS, even in a zero-shot scenario, proves comparable to CALF in full-shot settings. It highlights the greater transferability of visual modality to time series over textual modality.\n\n\n> Other Comments: The LLM have been recently proved not very necessary for time series [Are Language Models Actually Useful for Time Series Forecasting? NIPS2024]. So I wonder whether the vision model also suffers the similar situation?\n\n- We agree that text modality may offer limited benefit to time series. Following the paper's ablation study, we removed or replaced VisionTS's visual backbone with simpler modules. Appendix D.3 shows these changes degrade performance, indicating visual knowledge is indeed beneficial for TSF, unlike textual knowledge.\n\n**We hope these responses can fully address your concerns. Thank you once more for your detailed feedback!**", "claims_and_evidence": "The main claims of this paper is that the image and time series share similar properties. The author explains this points by an intuitive explanation. However, it is not very convincing to me since the domain gap between these two modalities. Further empirical evidence may be needed to justify why the pre-trained MAE can be used even in a zero-shot way to perform time series forecasting.", "methods_and_evaluation_criteria": "- The proposed methods mainly focus on the transformation of how to align the input space of image and time series. The solution of transforming to 2D gray scale image is reasonable. \n\n- The evaluation metrics are commonly used in existing works, which makes sense.", "theoretical_claims": "No theoretical proof included in the paper.", "experimental_designs_or_analyses": "- The experiments are solid. The authors have included various time series tasks, including zero-shot and few-shot, and have compared with both LLM-based and classic time series models.\n\n- I have noticed that the authors have discussed VisionTS can not model the interaction between multivariate time series data. Since the multivariate data is almost ubiquitous in real-world, this inability may weaken the practical usage. However, since this paper is a first attempt, this problem may be addressed in the future.", "relation_to_broader_scientific_literature": "The idea of using vision model for time series is novel, which can inspire further explorations.", "essential_references_not_discussed": "The experimental comparison lacks one highly related work CALF [CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning, AAAI2025], which is the existing SoTA LLMs-based time series forecasting works. How is the performance of the proposed VisionTS compared with CALF?", "other_strengths_and_weaknesses": "None", "other_comments_or_suggestions": "- The LLM have been recently proved not very necessary for time series [Are Language Models Actually Useful for Time Series Forecasting? NIPS2024]. So I wonder whether the vision model also suffers the similar situation?", "questions_for_authors": "None", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper presents VisionTS, which explores a very interesting idea: leveraging a vision masked autoencoder pre-trained on ImageNet for time series forecasting. By converting 1D time series data into 2D images, VisionTS reformulates forecasting as an image reconstruction problem. The experiments show that VisionTS delivers strong zero-shot performance and achieves state-of-the-art results with minimal fine-tuning on standard benchmarks.

Overall, all reviewers appreciate the paper’s novelty and creativity, as well as its extensive and rigorous evaluation across multiple benchmarks under both zero-shot and fine-tuning settings. Meanwhile, some concerns are raised, including: 1) more ablations are needed for understanding the efficacy of newly introduced components; 2) some competitive baselines (like TTM) are missing; 3) some important benchmark metrics are missing; 4) its scalability with large models is questionable; and 5) it is unclear how this method can generalize to time series data that exhibits multiple periodicity.

After considering the authors’ rebuttal, which effectively addressed most of these concerns, two reviewers increased their scores. Consequently, all reviewers now unanimously favor accepting the submission. The AC concurs with this decision.
Decision: Accept (poster)