Aurora: Towards Universal Generative Multimodal Time Series Forecasting
Authors: Xingjian Wu
Jianxin Jin
Wanghui Qiu
Peng Chen
Yang Shu
Bin Yang
Chenjuan Guo
Venue: ICLR 2026 Poster (2026)
Keywords: Time Series Forecasting
Multimodality
Primary area: learning on time series and dynamical systems

Abstract: Cross-domain generalization is very important in Time Series Forecasting because similar historical information may lead to distinct future trends due to the domain-specific characteristics. Recent works focus on building unimodal time series
foundation models and end-to-end multimodal supervised models. Since domain-specific knowledge is often contained in modalities like texts, the former lacks the explicit utilization of them, thus hindering the performance. The latter is tailored for end-to-end scenarios and does not support zero-shot inference for cross-domain scenarios. In this work, we introduce Aurora, the first Multimodal Time Series Foundation Model, which supports multimodal inputs and zero-shot inference. Pretrained on Cross-domain Multimodal Time Series Corpus, Aurora can adaptively extract and focus on key domain knowledge contained in corresponding text or image modalities, thus possessing strong cross-domain generalization capability. Through tokenization, encoding, and distillation, Aurora can extract multimodal domain knowledge as guidance and then utilizes a Modality-Guided Multi-head Self-Attention to inject them into the modeling of temporal representations. In the decoding phase, the multimodal representations are used to generate the conditions and prototypes of future tokens, contributing to a novel Prototype-Guided Flow Matching for generative probabilistic forecasting. Comprehensive experiments on 5 well-recognized benchmarks, including TimeMMD, TSFM-Bench, ProbTS, TFB, and EPF, demonstrate the consistent state-of-the-art performance of Aurora on both unimodal and multimodal scenarios.

Published as a conference paper at ICLR 2026
AURORA: TOWARDS UNIVERSAL GENERATIVE MULTIMODAL TIME SERIES FORECASTING
Xingjian Wu, Jianxin Jin, Wanghui Qiu, Peng Chen, Yang Shu, Bin Yang, Chenjuan Guo East China Normal University {xjwu, jxjin, onehui, pchen @stu. ecnu. edu. cn,
Jyshu,byange ciguo) @dase. .wnu.edd.com en
ABSTRACT
Cross-domain generalization is very important in Time Series Forecasting because similar historical information may lead to distinet future trends due to different domain-specific characteristics. Recent works focus on building unimodal time series foundation models and end-to-end multimodal supervised models. Since domain-specific knowledge is often contained modalities like texts, the for- mer lacks the explicit utilization of them, thus hindering the performance; and the latter is tailored for end-to-end scenarios and does not support zero-shot in- ference for cross-domain scenarios. In this work, introduce Aurora, the first Multimodal Time Series Foundation Model, which supports multimodal inputs and zero-shot inference. Pretrained on Cross-domain Multimodal Time Series Corpus, Aurora adaptively extracts and focuses on key domain knowledge contained corresponding text or image modalities, thus possessing strong cross-domain gen- eralization capability. Through tokenization, encoding, and distillation, Aurora extracts multimodal domain knowledge as guidance and then utilizes Modality- Guided Multi-head Self-Attention to inject them into the modeling of temporal representations. In the decoding phase, the multimodal representations are used to generate the conditions and prototypes of future tokens, contributing to novel catotype-Guidde Flow Matching for generative probabilistic forecasting. Com- prehensive experiments on 5 well-recognized benchmarks, including TimeMMD, TSFM-Bench, ProbTS, TFB, and EPF, demonstrate the consistent state-of-the-art performance of Aurora on both unimodal and multimodal scenarios.
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INTRODUCTION
Time series forecasting has gained sustained attention for decades of years due to its significant values in multiple domains, including economy, transportation, metcorology, and public health. In recent years, the key pivot comes with the surge deep learning, which brings the boom of merticulousll--designee deep forecasting models (Cirstea et al., 2022; Nie et al., 2023; Qiu et al., 2025e; Wu et al., 2025d). Through learning the inherent dynamics within the raw data, deep learn- ing models outperform classic statistical methods (Box & Pierce, 1970; Mei et al., 2014) and obey the scaling law (Shi et al., 2024a; Yao et al.). Due to the success, it also brings the most commonly-used forecasting paradigm, which utilizes the past information to infer how the series goes in the coming horizon. Although this paradigm contributes to impressive performance under domain-specific scenarios, its effectiveness uncertain when facing cross-domain inference, where similar historical information may lead to different futures due to domain differences.
As shown Figure/ 1, current research of time series forecasting explores the cross- noms-domain adap- tation in two main perspectives: 1) pre-training on cross-domain time series corpus for unimodal time series foundation models, which partially possess cross-domain generalization capabilities;

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2) utilizing cross modality information in train- ing end-to-end multimodal supervised mod- els, which effectively integrates domain knowl- edge in forecasting. For time series founda- tion models, the cross-domain generalization capabilities mainly come from the sensitivity to subtle differences in historical information from different domains. Some of them (Shi al.. 2024b; Liu et al., 2025e) are pretrained on trillion-scale corpus with heavy backbones, thus possessing certain cross-domain adaption capabilities. Others (Wang et al., 2025f;e; Wu et al., 2025a) have specific structures, which ex- cels at capturing cross-domain features. How-
Cross-Domait Multimodal I Time Series Corpus
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Stationarity
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Time Series Forecasters
End-in-Ead
Unimodal
Multimodal
DO0
PotckTST
Jnitiakzed
Backlons
Pethformer
Inttalized Backboas
+
GPTAMITS
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TimeVLM
ever, their capabilites come from single time modality and lack explict domain knowledge guidance, thus hindering the performance. For end-to-end multimodal supervised models (Jin et al., 2024; Liu et al., 2025b), though they con- sider the multimodal knowledge to enhance the domain-specific forecasting, they lack the abil- ity to support zero-shot forecasting in cross- domain scenarios. In our view, the aurora of next-generation time series foundation model lies in pretraining a cross-modality model on cross- domain time series corpus, which can utilize the domain knowledge within modalities and serve as a versatile out-of-the thettheboxx foreecaatrre forecaster in complex scenarios.
Protrainel
Sundal
Pretratoud
ROSE
Pntralsed
Bedkbeen
LighITT VisonTS
Backlosse
(Ouns)
Figure I: Aurora is pretrained on cross-domain multimodal time series corpus, supporting both text and image information to enhance zero-shot time series forecasting.
See Figure 1, we propose Aurora, which pioncers the exploration of multimodal time series founda- tion model. Specifically, we pretrain Aurora on time series data and sample-wise, domain-specific text descriptions. Since previous works (Chen et al., 2024a; Yu et al., 2025c) point out the endogenous images of time series contain additional geometric information, we also consider them into cross-modality learning. Considering the model architecture, Aurora adopts a novel cross-modality Encoder. Taking pretrained Bert (Devlin et al., 2019) and ViT (Liu et al., 2021) modality encoders, Aurora then adopts token distillation to extract the key information in different modalities. To effectively model the cross mross-modality interaction, we propose novel edge to adjust the attention of internal information within the time series data to obtain temporal features, and then fuse them with text and image features.
Multimodal Time Series Corpus, with
Sedalltt--uuide Self-Artention Mechanism to utilize the external domain knowl-
In the Aurora Decoder, we devise a novel flow flow-matching to fully utilize the fused modast features to support multimodal cross-domain generative probabilistic forecasting. First, we use a ConditionDecoder to generate multimodal conditions for flow matching. Since the future trend of time series is often implied by external text information, and the inherent periodicity of time series is often contained in the endogenous images, we then design Prototype Bank initialized by Period and Trend prototypes, and leverage a PrototypeRetriever to retrieve the "future prototypes" based on the inherent domain knowledge from texts and images. Compared with DDPM (Ho al., 2020), Flow Matching (Lipman et al., 2023) serves as a stochastic interpolant, which can start from a random distribution instead of Gaussian noise, with more flexibilities. We take the generated future prototypes as starting points, which contains the rudiments of periodicity and trend for future tokens, thus can simplify the flow matching process. Our contributions are summarized as follows:
We propose a multimodal time series foundation model, called Aurora, which is pretrained on cross-domain multimodal time series corpus and supports generative probabilistic forecasting. Through effectively fusing multimodal information during pretraining, Aurora serves as a strong zero-shot forecaster, and can make accurate cross-domain inference.
We devise novel modallty encoder in Aurora, consisting of token distillation and modal- ity guiding, implemented by merticulously-desipee attention structures. It can enhance the temporal representations while effectively fusing representations from texts and images.
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We design novel nlw-mnathhing process in the Aurora Decoder. It obtains multimodal condi- tions through a Transformer, and obtains future prototypes containing periodic and trend infor- mation as the starting points, thus enhancing the ability of flow-matching.
Experimentally, Aurora achieves state-of ofate-of-hhe-at performance marks, including datasets from TimeMMD (Liu et al., 2024b). TSFM-Bench (Li et al., 2025c), ProbTS (Zhang al., 2024), TFB (Qiu al., 2024), and EPF (Olivares al., 2023), cover- ing comprehensive scenarios, i.e., unimodal, multimodal, deterministic, and probabilistic, thus demonstrating strong -ut-of-the-box tool of decision intelligence.
5 well-recognized bench-
RELATED WORKS
2.1 TIME SERIES FORECASTING
Time Series Forecasting is vital in decision-making and has fascinated people for decades of years, which facilitates the emergence of a series of works. recent years, deep-learning models are widely studied, among them. Autoformer (Wu et al., 2021), Triformer (Cirstea al., 2022), Times- Net (Wu et al., 2023), Pathformer (Chen et al., 2024b), PatchTST (Nie et al., 2023), Dlinear (Zeng al., 2023), FiTS (Xu et al., 2024), SparseTSF (Lin et al., 2024a), PDF (Dai et al., 2024a), DUET (Qiu et al., 2025e), and TimeMixer++ (Wang et al.), continuously advancing the state-of- the-arts. However, though they possess the capabilities to extract the inherent dynamics in raw time series data, they only adapt to unimodal end-to-end forecasting scenarios, and often fall short in multimodal forecasting scenarios where the domain knowledge widely contained in the text modality.
Recently, some works are proposed explore the multimodal end-to-end supervised models. In summary. they utilize Large Language Models' strong reasoning capabilities to integrate textual domain knowledge to prompt temporal modeling. Among them, Unitime (Liu et al., 2024c), Time- LLM (Jin et al., 2024) and CALF (Liu et al., 2025b) utilize the endogenous textual descriptions as prompts, GTP4MTS (Jia et al., 2024), TATS (Li et al., 2025d) and TimeMMD (Liu et al., 2024b) supports exogenous textual domain knowledge. However, they do not possess generalization capa- bilities in zero-shot scenarios.
2.2
TIME SERIES FOUNDATION MODELS
To support cross-domain generalization, unimodal Time Series Foundation Models are widely stud- ied. The majority of them adopt Tranformer-based architectures, which are pretrained time ries corpus billion- or trillion- scale to obtain the strong generalization capabilities. Among them, Sundial (Liu et al., 2025e), VisionTS (Chen et al., 2024a), ROSE (Wang et al., 2025f), Light- (Shi et al., 2024b). MOIRAI (Woo et al., 2024), TTM (Ekam-
GTS (Wang et al., baram et al., 2024), Chronos (Ansari al.), UnITS (Gao et al., 2024), Timer (Liu et al., 2024c), and TimesFM (Das et al., 2024) demonstrate strong zero-shot forecasting performance on unimodal tasks, outperforming those full-shot supervised models in many cases. Considering the fore- casting paradigm, Sundial, MOIRAL, Chronos, and Lag-Llama (Rasul et al., 2023) also support probabilistic forecasting, which provides additional robustness and versatility for decision-making. Despite their endeavors to enhance cross-domain generalization capabilities, when historical series exhibit similarities, the forecasts they generate remain static. This lack of adaptability renders them unable to accommodate the diverse and changing real-world domains.
In this work, we propose Aurora to pioneer the exploration of multimodal time series foundation models. Through pretraining on Cross-Domain Multimodal Time Series Corpus, Aurora can extract the key domain knowledge within the text and image modalities to enhance the modeling of temporal features. Aurora also supports generative probabilistic forecasting, thus covering versatile tasks, including unimodal, multimodal, deterministic and probabilstic forecasting.
3 AURORA
In this work, we pretrain Aurora in cross-modality paradigm, which adopts Channel- Independence (Nie al., 2023) time series data, and models corresponding multimodal inter-
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text
9:00 o.m. NVIDIA onnounces a edFinership with OpenAI to integrate its -generation GPUjiinto lorge-scale AI troining Infregetsccuuee
Tokenizer
TextEncoder
TextDistille
TextGuider A
Stine
X
time
Tokenizer
MSA
Add&Nonm
FFN
AddāNorm
Madality
Fuser
time
X
Modality-Guider Multi-
image
head Self-Attention
VisionSuider A
X/use fase
Tokenizer
VisionEncoder
Visionbistiller
Aurora
Encoder
penoos
X Viimage
X
Period
Sinusoidal
TNNWIN
Time
PrototypeRetriever
MM
Noise
Flow- Matching
Prototype
Prototype Bank
ConditionDecoddr
X
Juse
Mask
RoPE
I
Condition
Velocity
Ado&Norm
Add&Norm
Add&Norm
Add&Norm
2
MSA
FRN
MCA
FFN
Prototype- Guided Flow Matching
X
Aurora Decoder
Cross- Transforrne
cond
Figure 2: The overview of Aurora. In the Aurora Encoder, the multimodal information is extracted, distilled, and fused. Modality-Guider Multi-head Self-Attention is introduced to inject the domain- specific knowledge into temporal modeling. In the Aurora Decoder, Prototype-Gune Flow Matching is introduced support generative probabilistic forecasting.
action to inject domain knowledge. Note that each variable of time series is first normalized through Instance Normalization (Ulyanov et al., 2016) to mitigate the value discrepancy, As shown in Fig- ure 2, Aurora mainly consists of two phases: 1) in Aurora Encoder, we tokenize and encode each modality into modal features, then fuse them form multimodal representations; 2) Aurora De- coder, we utilize a Condition Decoder to obtain the multimodal conditions of future tokens, leverage a Prototype Retreiver to retrieve the future prototypes based on the domain knowledge, and conduct flow matching them to make generative probabilistic forecasts.
3.1 ENCODING
3.1.1 MULTIMODAL TOKENIZATION
Aurora inherits the strong encoding capabilities from VIT (Liu al., 2021) and Bert (Devlin et al., 2019) to extract the representations from images and texts, and adopts temporal Channel- Independent Transformer the main backbone. Therefore, inputs of all modalities required to be tokenized first.
Given a univariate time series X RT we adopt RevIN (Kim al., 2021) technique mitigate the inherent non-stationarity of time series. The time series tokens Xiiee are formed through non- overlapped Patching and Embedding (Cirstea al., 2022: Nie al., 2023):
X' LeftPad(X), x PatcchingXX') R= time xptime
time Embedding(X*)) E I time iime
(2)
where Embedding is a linear projection, time E I time time
are the embeded time series tokens,
with time representations of dimension dime
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To obtain the endogenous image tokens, we utilize the rendering techniques (Chen et al., 2024a) to make the transformation:
A = Amp(FFT (4) [T/F],
(3)
X LeftPeriodPPa(( X2D Reshape X) R
(4)
X = Resize(Repeat( (X x2D)) E R R
(5)
X 3D ImagePatching(X**P) I
image pinuge
p penuae
(6)
X Ximage Embedding(Fllaten( (Flatten((XX))) E
R
imoue x dimage
(7)
where the time series is first processed into 2D structure X E RXXP based on the period P.
Then the endogenous image X3D E 3xxxx is rendered through repeating X along channel dimension, and resizing into the standard input size of ViT. Finally, the image tokens Ximage R" image diimaage
obtained through ImagePatching and Embedding.
For the corresponding texts, the text tokens Xteart R text xd
can be easily obtained through
tokenization and retrievement from the vocabulary of Bert.
3.1.2 TOKEN DISTILLATION
After obtaining the tokens from all the modalities, the hidden representations of texts and images then generated through the pretrained VisionEncoder (VIT) and TextEncoder (Bert):
X iimage VisionEncodeer( Xmmgge) E R genimege image
(8)
X text_ TextEncoder(XX X
R
xdtest
(9)
Inituitively, there exists informative redundancy in texts and images for multimodal time series fore- casting. For texts additional domain knowledge, key descriptions which can affect the future trend of time series often deserve only several words. For the endogenous image, we consider it as technique extract the varying inherent periodic information time series data from multiple domains, where the information is also sparse. Therefore, we distill the tokens from text and image modalities to extract the key information and improve the efficiency:
Ximage VisionDistiller( Rimage, Ximage) E IR Krimase xdimage
(10)
X tent TextDistillerr Reeatt Xtext) E IR K xdfuet
(11)
where VisionDistiller and TextDistiller are based on the Multi-head Cross-Attention Mechanism.
The Rimage E RR. K.Ke mage and Riewt E TRK*==tx'' dd are learnable vectors (with Klext < next Kimaag < immgge)) which are the queries and can serve semantic clustering cen-
troids (Zhang & Yan, 2022) to help compress the information in X Eimage and Xteezt And X Ximage and Elezt are the distilled image and text tokens.
3.1.3 MULTIMODAL ALIGNMENT
In multimodal time series forecasting. the time series modality occupies the dominant role and information from other modalities can serve as domain-specific knowledge to guide the extraction of temporal representations, thus enhancing the cross-domain generalization capability. In Aurora, we explicitly implement the above informative flow through Modality-Guided Multi-head Self- Attention mechanism. First, we capture the correlations between the time series modality and others through Cross-Attention based VisionGuider and TextGuider:
VAttn VisionGuider time Ximage) E
R time K imagg
(12)
TAtm TextGuider( X time Xtext
E R time x x
(13)
Corr VAttn W TAAtn E R time ti
time
(14)
where VAttn and TAttn are unnormalized attention scores, separately denoting the correlations be- tween time modality and image or text modality. Corr E "n xn time denotes the inherent correla- tions inside the time series modality, bridged through the text-image comelations. We also introduce
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W E &R" RK x K as learnable metric (Qiu al., 2025e) to further tune the semantic distances.
Therefore, this process helps bridge the correlations between time series tokens via a perspective of multimodal domain information. We then inject Corr into the temporal encoding process:
Q = fime Wº, K
X W
X W
(15)
S (Q KJ Corr)/ diime O Softmax(S)
(16)
O Onorm LayerNorm (X time + O),
(17)
Xtme LayerNorm(feeFForwardd (oorm)) + O
(18)
where Wº, W WV E Rd'"m== xd
Xtime E Rn tm denotes the generated temporal
representations. The Corr matrix contains domain knowledge, which guides the attention scores to focus on the appropriate time series tokens (empirically validated in Section C.4). Finally, we fuse the representations from three modalities through Cross-Attentie based modality fuser:
X immgg CrossAttn (X Xtime X
RR" time dime
(19)
Ntett = Cross x(ext) E IR nite x dimime
(20)
X Ximme + Ximagge Xtent
(21)
where Fuse E Rn time iime
are the fused multimodal representations.
3.2 DECODING
3.2.1
CONDITION DECODING
Inspired by DiT (Peebles & Xie, 2023), we utilize an L-stacked Transformer to decode the condi- tions of future tokens, which helps construct the stable Flow Matching process. Specifically, the ConditionDecoder consists of a Causal- Toansll-Taafformee and Cross- Transformer:
X cond Causal Grussl-TTaansommeeeeeep((((XX F)),
(22)
X
Cross-Transformett eeond x
(23)
where F denotes the number of future tokens. The last token of fuse is first copied F times and fed to the Grusal-Transformett generate the future conditions X Xcond R P' then adopt integrated with RoPE (Su et al., 2024) to further refine them into Xcond E I Fxddiime where Xfuse is set as Key and Value embeddings. Therefore, the ConditionDecoder can efficiently output all conditions.
a
3.2.2 PROTOTYPE-GUDDE FLOW MATCHING
Target
Token
Algorithm 1 Prototype-Guided Flow Matching
1: Given condition X steps J. and Prototype P..
Prototype Token
3/2 (0)
(1)
2: Sample a noise & ~ N,,
9
3: At = 1/J. 1 h2 = Xpond 9) = P, +
for j in {0, 1 ...,. J 1} do
Figure 3: Prototype-Guided Flow Matching. The starting point is set a prototype instead of random gaussian noise, which provides an intu- itive guidance in generation process.
5: Ú: di + a. a (0. (h. At
6: end for
7: Return: 9
Different from DDPM (Ho et al., 2020), which can be treated as an SDE solver to transform data from fixed Gaussian distributions to realistic target distributions, Flow Matching (Lipman et al., 2023) serves as a more intuitive and smooth ODE solver, which learns the Velocity Field between a random initial distribution and the target distribution. However, current methods (Liu et al., 2025e;

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Kollovich et al.) still set the initial distributions as Standard Gaussians, which neglects the capa- bility of Flow Matching to work like a stochastic interpolant. Obviously, constructing appropriate prototype as the inital starting point can enhance the intuitiveness and stability of Flow Matching.
Based on the motivation that the future trends and periodicities of time series mainly rely on the multimodal domain knowledge in texts and images, we intuitively devise Prototype Bank and PrototypeRetriever to adaptively construct initial prototypes for Flow Matching. The Prototype Bank P E R Mxp contains M learnable period and trend prototypes, initialized through trigonometric, exponential, logarithmic, and polynomial bases. The Transformer-basedd PrototypeRetriever receives the text representations Xtent and image representations Ximage as inputs, considers the positional information of future tokens through Sinusoidal Embeddings (Vaswani et al., 2017), and outputs the categorical distributions of the all M prototypes through Softmax:
D PrototypeRetrieve((X X teat X Ximage) E ER. Fx
(24)
where D denotes the weights of prototypes, we then generate the new prototypes through: P time where the generated prototype P contains the approximate future periodicities and trends. As shown in Figure 3, the motivation of Flow Matching is fit the velocity field be- tween the initial prototype yi (0) = P: + 4 and the target horizon y, = yí, where Yt E RR'" is the groundtruth of the i-th future token, and e; (O, I) is used to increase the diversity during train- Network with an MLP structure and utilize the AdaLN (Peebles
D. E Fxp'in
ing. We design the Flo--Matchin & Xie, 2023) integrate the multimodal conditions h2 Xçond, We adopt the conditional optimal- transport path, which is energy-optimal and contributes to a uniform velocity field. And the function
of Flow-Matching Network ey is to predict the velocity based on the current position y, and con- dition h5. To achieve this, the token-wiss wise optimization objective L is designed as:
(s) |01 (v) (1) h4a) (ha) (y, (1) C(0, h4) = E. (or wn
vi )1
(0)
(25)
where / E [0, 1). v/") 0 denotes the targeted fixed velocity field. w/ - -t (1) + (1 ()y, (0)
is the expected position in the uniform velocity field at moment t. The objective is to tutor the Flow-Matching Network of output the velocity when given the position and condition.
In the inference phase, the sampling process is discretized integration process-see Algorithm 1.
The Gaussian noise 6, I), helps support probabilistic forecasting. Finally, we can obtain the forecasts ÿt E IRF of the i-th future token. And the forecasts of the future horizon are Y = Concat (1} E FF
4 EXPERIMENTS
We make extensive experiments to evaluate the performance of Aurora. Specifically, we intro- duce the experimental settings in Section 4.
Unimodal Forecasting
Multinodal Forecasting
Zero shot On TSFM TSFM-Bench Bench
Zere-shot TimAMAD
151% MSS] va Time-
37.0% MSE, vs Sandil
MSR] Rank 1 MSE MAE
31.2% MSE vo VokuTS
In Section 4.2, we evaluate the zero-shot and few-shot performance of Aurora on multimodal forecasting scenarios. Considering the modal absence in the realistic world, we also evalu- ate the zero-shot performance of Aurora on uni- modal forecasting scenarios-see see Sections 4.3, 4.4. To analyze the key components in Aurora,
Rack: MSE MAT.
Zero shot On ProbTS
Few-shot on TimeMMD
AUMPU
CRPS)
12.% MSF, GPPPTMSSS
20.3% CHPPS) MCHEAL
CALF
Rask CRPS & NMAE
Rack MSE MAE
Figure 4: Evaluation summary of Aurora.
also make detailed model analyses in Section 4.5. In summary-see Figure 4, our proposed Au- rora achieves sfate-of -f-te-of-thh-ar performance in both unimodal and multimodal forecasting scenarios.
4.1 EXPERIMENTAL SETTINGS
Cross-Domain Multimodal Time Series Corpus. We first collect a substantial number of open- source time series datasets across diverse domains, then generate the corresponding sample-wiss textual descriptions using Large Language Model (Liu et al., 2024a), which simulates the down- stream scenarios with domain-specific textual information.
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Benchmarks. We evaluate both the multimodal forecasting and unimodal forecasting performance of Aurora on 5 benchmarks, including TimeMMD (Liu et al., 2024b), ISFM-Bench (Li et al,, 2025c), ProbTS (Zhang et al., 2024), TFB (Qiu et al., 2024), and EPF (Wang et al., 2024b). Note that these benchmarking datasets are strictly excluded from the pretraining time series corpus.
Baselines. We compare Aurora with 11 well-known unimodal time series foundation models, in- cluding Sundial (Liu et al., 2025e), VisionTS (Chen et al., 2024a), ROSE (Wang et al., 2025f), Time-MoE (Shi et al., 2024b), MOIRAI (Woo al., 2024), TTM (Ekambaram et al., 2024), TimesFM (Das et al., 2024), Timer (Liu et al., 2024e), UniTS (Gao et al., 2024), Chronos (Ansari et al.), and Lag-Llama (Rasul et al., 2023). We also consider multiple strong end-to-end super- vised models, including multimodal ones like GPT4MTS (Jia et al., 2024). TATS (Li et al., 2025d), CALF (Liu et al., 2025b), and Vime-VIM (Zhong et al.), and unimodal ones like TimeXer (Wang et al., 2024b), PatchTST (Nie et al., 2023), iTransformer (Liu et al., 2024d) TSDiff (Kollovieh et al., 2023), CSDI (Tashiro et al., 2021), TimeGrad (Rasul et al., 2021a), and GRU MAF (Rasul et al., 2021b). The detailed information is provided in Appendix A.
4.2 MULTIMODAL FORECASTING
We compare the zero-shot forecasting performance of Aurora with unimodal Foundation Models, and compare the few-shot (10%) forecasting performance with Full-shot Multimodal End-to-end Supervised Models. As shown in Table 1, compared with unimodal foundation models, Aurora ob- viously possesses stronger generalization capability by achieving most 1st counts. Compared with previous state-of-the-arts Sundial and VisionTS, Aurora achieves average MSE reduction of 27. 77.0% and 31.2% on TimeMMD. When compared with Full-shot Multimodal End-to-end Supervised Mod- els, Aurora trained on only 10% of data and outperforms all baselines in most settings. Compared with well- kell-known baselines like GPT4MTS and CALF, Aurora achieves average MSE reduction of 12.8% and 24.5%. On some datasets such as Climate and Environment, even the zero-shot perfor- mance of Aurora has outperformed those full-shot baselines. These empirical evidences can provide strong support of Aurora's 's the multimodal generalization capability.
Table 1: Average results of multimodal zero-shot few-shot forecasting experiments on datasets from TimeMMD. Lower MSE or MAE values indicate better predictions. Red: the best, Blue: the 2nd best. All the results are listed in Table 12 of Appendix B.
lew-bot
Fall-she Mutiinodal
Supariad Modils
Model
Aunes (Ouro
Sundial (30ba)
VidantS (1062)
(2003)
RUSE:
MOIRAI (2034)
Aunura
(Orro)
GPTDMIS
(2025)
(2102)
TATS
CALI
(1862)
TimeVEMM
(2002)
Mariix
MSR
MAP
MSR
MAR
MSP
MAR
MSR
MAR
MSF
MAR
MSE
MAR:
MS:
MAR
MSR
MAR
MSP
MAR
MSS
MATI
Agricature
0.322
0.31
0.333
0.390
0.300
0.336
0.345
0.372
2,271
0.408
0.213
0.293
0.225
9.231
0.115
0.301
0.500
0315
0.337
0.302
Classc
0.665
bounstry
0363
0.546
0.201
0.032
11301
0.602
1.389
06535
0.405
0.312
0.116
0/999
U10
0.003
01017
0.004
0.007
10044
0122
0.000
0.276
6.31%
0.366
0.416
0.55
0.436
0.390
0.656
0.35)
0.400
0.365
0.373
0.503
0,800
0.319
0.365
0.355
0.582
0.310
0.397
Healh:
1,53
0,850
1,000
0,992
2.46
2.596
1.300
2.736
1.241
0.799
1.35
0.767
1,491
0.775
1.459
Spowray
12445
4384
70:44
40015
199.995
4.59
99.324
4.765
93.245
5,113
701662
3,988
11.457
4.055
72.405
4187)
76.315
4,300
23.731
4.181
Social Good
0.518|
1.06
0.521
1.126
0.61%
1.341
11561
1440
01851
0,014
0.164
0.33
0.410
11915
0.414
0500
0.411
01958
0.444
Troffo
0.161
6,269
0.201
06381
0
03311
0.551
0.606
0.M1
0.236
0.200
039
Coust
31
26
4.3 UNIMODAL FORECASTING
Considering the modality absence phenomenon in many downstream scenarios, Aurora also sup- ports forecasting without textual inputs through random masking in the pretraining phase. And endogenous images can be always obtained from raw time series. To evaluate the unimodal zero- shot forecasting performance, we conduct experiments on TSFM-Bench and ProbTS. As shown in Table 2-3, Aurora achieves state-of-the-art performance on both deterministic and probabilistic forecasting tasks. Compared with Time-MoB and ROSE, Aurora achieves average MSE reduction of 15.1% and 22.9% on TSF.MBeench, demonstrating strong deterministic forecasting capability. When evaluated probabilistic forecasting benchmark ProbTS, Aurora also outperforms CSDI and MOIRAI with average CRPS reduction of 21.5% and 38.3%. Aurora is proven to be the best unimoda time series foundation model, ensuring the robustness when modality absence occurs.
8

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Published as a conference paper at ICLR 2026
Table 2: Average results of unimodal zero-shot deterministic forecasting experiments on datasets from TSFM-Bench. Lower MSE or MAE values indicate better predictions. ('-") denotes datasets included in the model's pretraining and therefore excluded from testing. Red: the best, Blue: the 2nd best. All the results are listed in Table 13 of Appendix B.
13pe
4 Zep shot Foundation Models
Medels
Aarora
(Oun))
Sundial (3025)
RUSE (2023)
Timee (2004)
TimesEM
(3023)
Chrenos
(3024)
Time-Mol
(2024)
UNITS
(2004)
MOIRAL
(2004)
(3004)
ITM
Mosics
MAE
MSE
MAE
MSE
MAE
MSE
MAE
M5E
ETT [Aggs
0.33
0.376
0,335
0.379
0,343
0.411
(955)
0.478
0.415
0,406
0,442
0.405
0,357
0,310
00471
0.437
[ 19.343
0.358
[1.44
0,430
Westter
0.250
0.267
0,234
0.209
0,365
0.305
[
00209
0.313
0.258
0.309
0.256
0.289
0.275
0.298
[ 0,3000
0.275
0.255
0.307
Heeenicty
0.375
0.265
0.734
0.300
(0207
10/00
0.21
| 00188
0.20%
Tramfi
0.508
0.412
Solar
0.205
033890
00721
0.282
0,905
0.540
[ [0771 01608
11900
0.99
0..99
0.319
0AT
0.0%%
0.669
0.704
[1815
0.710
PUMSUUS
0.563
0,853
1,309
0.79
0.002
1,845
0.907
1.207
1,004
1,253
0.09
1,200
1,000
Wind
0.772
1,251
| 0.763
1.500
NYSE
0.528
0.536
0.860
0.642
0.966
0.704
[1623
0.536
1.129
0.720
1,220
0.820
1" Count
27
21
11
11
3
0
Table 3: Average results of unimodal zero-shot probabilistic forecasting experiments on datasets from ProbTS. Lower MSE or MAE values indicate better predictions. (-"" denotes datasets included in the model's s pretraining and therefore excluded from testing. (e²) denotes it takes too long time to run. Red: the best, Blue: the 2nd best. All the results are listed in Table 14 of Appendix B.
Type
shot Foundaioo Models
O Full-sot Probubllste end Supvessee Models
Madies
Aurora
(Oun))
Sundlal (2025)
Chrenos (2024)
MOIRA (2024)
LogiLloma (2025)
TSDIT
(2023)
USBI (2022)
TineGrod (2022)
GRU MAY
(2021)
Metric
CRPS
NMAE
CRPS
NMAE
CRPS
NMAE
CRPS
XMAE
CRPS
NMAB
CRPS
XMAE
CRPS
NMAE
CRPS
XMAE
CRPS
NMAE
(Arg)
0.221
0.379
0.73
0.370
Westhe
0.070
0.876
0.067
0.102
0.142
0.15K
0.179
0.143
0.006
0.106
0.132
0.134
0.072
0.003
0.125
0.155
0.133
0.165
Elecsiity
0.855
0.108
0.081
0
0.347
0.290
0.407
0.519
0.102
0.125
0.094
0.122
Trathe
0.362
0.269
0.295
0.30
0.327
0.225
Rechange
0J944
0,047
0.045
0.999
0.065
0.042
0.045
0.000
0.057
0.089
0.044
0.111
0.969
0.086
0.662
0.095
0.000
0.85
11I1
0.147
0.166
0.108
0.165
01700
0.197
0.19
0.156
0.211
0.348
0.289
0.276
0.750
0.764
0.310
0.322
0.748
Cuunt
4.4 SHORT-TERM FORECASTING
We also consider the scenarios with insufficient historical time series data, for which foundation models are better suited than end-to-end models. To evaluate Aurora in such scenarios, we simu- late with short-term forecasting, where the contextual series is very short and limited for training- Specifically, we conduct experiments on EPF (Wang et al., 2024b) and univariate datasets from TFB (Qiu et al., 2024). As shown in Table 4, Aurora outperforms most-advanced Foundation Mod- els such as Sundial and VisionTS in most evaluations. Compared with full-shot supervised models like XireXer, and iTransformer, Aurora also achieves competitive performance with them. Focusing on more scenarios, i.c., the 8,068 univariate datasets in TFB see Figure 5, we report the mean MASE and msMAPE results, which indicate that Aurora also achieves state-of-the-ar art performance against zero-shot Foundation Models, and full-shot supervised models with versatile neural structures. All of the experiments demonstrate Aurora's strong capability in short-term forecasting scenarios.
Table 4: Results of short-tern zero-shot forecasting experiments on datasets from EPF, Lower MSE or MAE values indicate better predictions. ("-") denotes datasets included in the model's pretraining and therefore excluded from testing. Red: the best, Blue: the 2nd best.
Type
Zero shot Foundation Models
c Full shor End end Supervised Models
Modell
Aurora (Ours)
Sundial (2025)
VisionTS (2025)
ROSE (2025)
MOIRAI
(2024)
TimeXer
(2024)
fTrandormerr
(2024)
PatchTTT (2023)
TimesNet
(2023)
Metrics
MSB
MAB
MSB
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
NP
0.288
0.312
0.256
0.272
0.100
0.461
0.666
0.536
0.660
0.538
0.238
0.268
0.265
0,300
0.267
0.284
0.200
0.289
PIM
0.064
0.183
0.088
0.189
0.251
0.311
0.402
0.423
0.088
0.097
0.209
0.195
BE
0.361
0.152
0.371
0.270
0.679
| 00815
0.514
0.837
0.534
0.241
0.394
0.403
0.264
0.288
TR
0.367
0.306
0.392
0.207
0.625
|
0.746
0.447
0.751
0.454
0.211
0.439
0.411
0.220
0.234
DC
0.539
0.475
0.541
0.484
0.961
1.276
0.778
1,251
0.779
0.440
0.418
0.479
0.461
0.432
0.916

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Published as a conference paper at ICLR 2026
CRN
Tranaformer
Foundation
Our
Tranforren
Foundation
2.0
2.28
2.14
2:34
2.4 3.0
AJ9 2.12
29-7
255
22
241
2314
3.5
28
nerure
Thes
Fomttal
dereva
natere
(a) Mean MASE results.
(b) Mean msMAPE results.
Figure 5: Mean MASE and msMAPE results of 8,068 univariate datasets in TFB. The full results can be found in Table 10 and 11 of Appendix B. Red: the best.
4.5 MODEL ANALYSIS
Ablation Studies. Based on the Modality- Guided Multi-head Self-Attention, Aurora can utilize the domain knowledge contained in text and image modalities to model the tem- poral features. To validate its effectiveness, make ablations on it by setting Vari- ant 1, which adopts original Multi-heaa multi-head Self- Attention. Considering the Prototype-Guu Guided Flow Matching, which can generate prototypes of future tokens to simplify the generation pro- cess, we make Variant 2, which does not uti- lize the prototype mechanism and sets the ini- tial distribution as Standard Gaussian. Natu- rally, we also make Variant 3, which eliminates both of them. As shown in Table 5, results show that each above-mentioned module is indispensable, and a cascading effect occurs when both mod-
Table 5: Ablation studies on without Modality-
Guided Multi-head Self-Attention (Variant D), without Prototype- Guided Flow Matching (Vari- ant 2), and without both of them (Variant 3).
Aureeu
Yariuut
Variant 2
Mentics
MAE
MSE
MAB
MSE
MAE
MSE
0.348
0.351
0.290
0.334
0.324
Climate
0865
0.749
1,176
0,888
1,088
1.447
Econonsy
0J333
0.146
0,77
0,419
0.945
0.172
0.26
0.440
Fnerg
8258
1.370
0.768
0.381
0357
0.872
0.72
0.388
Limiromen
0.276
0.379
0.324
0.398
0.354
0.411
0.388
0.499
(U850)
1.757
0.936
1.588
0.876
2,447
Sacurity
Social Good
0.516
1002
0.548
1,425
1.457
Toalks
0.161
0.289
0.244
0.378
0.273
0.418
0.335
0.467
ules removed, where the performance crashes when the modules are removed.
Inference Scability. Adopting a generative probabilistic head, Aurora makes forecasts based on multiple sampling-see Algorithm 1. So that we study the scability of Prototype- Guided Flow Matching by exploring the corre- lations between the sampling number and fore- casting performance in Figure 6. Specifically, experiments are conduct on ProbTS, where the average values of CRPS and NMAE across all datasets are reported. The results indicate that both CRPS and NMAE demonstrate consis- tent improvement as the sampling number rises. They attain good performance when the sampling number reaches 100, showing obvious inference scability, and moderate efficiency-see Section A.7.
0.300
0.628
0300
0.253
0.40
2246
0289
00.14
0.168
0.198
0.16c
0.190
0303
3300
Sampled Predictions
Sampled Predictions
Figure 6: Sampled Predictions.
CONCLUSION
In this work, we introduce Aurora, the first multimodal time series foundation model. To sum up, Aurora adopts a merticulously designed Modality-Guided Self-Attention to capture temporal dynamics, and novel Prototype-Guided Flow Matching to enhance forecasting performance and supporting generative probabilistic modeling. Comprehensive experiments on unimodal and mul- timodal forecasting tasks, including well-recognized benchmarks, demonstrate that Aurora is strong out-of-the-box tool for decision intelligence.
10

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Published as a conference paper at ICLR 2026
ACKNOWLEDGEMENTS
This work was partially supported by the National Natural Science Foundation of China (62372179, 62472174), and ECNU Multifunctional Platform for Innovation (001). Chenjuan Guo is the corre- sponding author of the work.
ETHICS STATEMENT
Our work exclusively uses publicly available benchmark datasets that contain no personally identi- fiable information. The Pross-Domain Multimodal Time Series Corpus used to pretrain Aurora is also collected from public datasets, and integrated with LLM-generated textual descriptions, also containing personally identifiable information. No human subjects involved in this research.
REPRODUCIBILITY STATEMENT
The performance of Aurora and datasets used in work real, and all experimental results be reproduced. We have released our model code and checkpoints on Github and Huggingface.
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THE USE OF LARGE LANGUAGE MODELS (LLMS)
In this work, we only adopt Large Language Models in our methodology and data generation. Specifically, we leverage Bert as the TextEncoder of Aurora to extract the textual features. To generate the textual descriptions for the cross-domain multimodal time series corpus, we provide domain descriptions and raw time series for GPT-4, encouraging it generate the descriptions of data characteristics, which are only used for pretraining Aurora. Note that we do not use Large Language Models in writing.
A EXPERIMENTAL DETAILS
A.1 CROSS-DOMAIN MULTIMODAL TIME SERIES CORPUS
Cross-Domain Time Series Corpus. To pretrain Aurora, we initially make use of an extensive compilation of time series datasets. These datasets are soureed from multiple origins, encompass- ing specific subsets from repositories such as ERA5 (Liu et al., 2025e), loT (Liu et al., 2025e), Monash (Godahewa al., 2021), UEA (Bagnall al., 2018), and UCR (Dau et al., 2019), well as several well-established benchmarks (Zhang et al., 2017; Wang et al., 2024a; Liu et al., 2022; McCracken Ng, 2016; Taieb al., 2012). comprehensive list of these datasets presented in Figure 7, containing more than billion time series points. We take care ensure that there is no overlap between the pre-training datasets and those employed in downstream evaluations. It should be noted that while both the pre-training and target sets incorporate weather, Energy, Health, and Economy data, they are from different sources.
Cross-Domain Time Series Corpus
Datasets
#Points
SSurce
ERAS
133,556,790
Sundial
1oT
99,414,450
Sundial
Cloud 9.9%
Aus. Ele Demand
1,155,264
Monash
Wind
Monash
Energe 18.8%
Wind Farms
172.178,060
Monasb
Solar Power
7,397,222
Monash
London Smart Meters
166,527,216
Monash
Weather 13.3%
MD
TFB
Economy 0.9%
Biscoin
75,364
Monash
NNS
87,801
Motorlmagery
72,576,000
UEA
Selfi SgufRoggla
6,080,256
AtrialFibiillaioon
38,400
UEA Wang al., 2024b
TDBrain
79,252,705
Health 15.9%
PEMS
$4,226,082
BAY. 03, 04, 07
Truffic
116,485,589
Monash
Phoneme
2,160.660
UCR Zhang 2017 2017
PRSA
4,628,448
Nature 24.2%
Worms
252,200
Transport 5.4%
Songeen
23,741
Monash
KDD Cup 2018
2,942, 364
Monash
7,305
Monash
Web 11.6%
Figen Worms
27,947,136
UEA
LightCurves
9,457.664
UCR
Sunspor Temperature Rain
73,924
Monash
23,252,200
Figure 7: Introduction of time series data sources Cross-domain Multimodal Time Series Corpus.
We list the ratios of different domains and report detailed dataset sources and lengths.
Textual Descriptions. Since multimodal time series data is scarce in the real world, current works (Liu et al., 2024b; Kong et al., 2025; Xie al., 2024) often construct corresponding textual information based on human experiences and Large Language Models, which is proven effective in training models. Following their paradigm, we provide raw time series data in Figure with domain descriptions, encouraging GPT-4 (Achiam et al., 2023) to heuristically generate textual descriptions of sample-wise time series. thus obtaining high-quality multimodal time-series data from simula- tion. Specifically, after GPT-4 agent generates the textual descriptions, we first coarsely check the quality with another GPT-4 agent. the quality is low, the process will be reset. After a batch of textual descriptions generated, randomly sample from them and check the quality manually,
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then determine whether to regenerate this batch of data and tune the prompts. As shown in Figure 8, here's some samples of our prompts for GTP4 and the generated texts:
This time series segment records a span of 2000 seconds, reflecting the road flow of some Interstate Highway. Here's Accident report about this time series, _ you refer to it. Please analyze the future trend and characteristics of this time series, and output textual descriptions with more than 200 words.
Prompt:
The width of the road in the Interstate Highway Accident Report was carrying low traffic volume, with almost 70 percent of the road being affected. Corresponding time series has experienced drastic fluctuations. It started with high value of 22.7, then dropped sharply to low value of 2.3, and after that, it further decreased to low value of 0.1. Overall, this time series shows sudden change, including significant decline and continuous downward trend,
Generated Text:
(a) Case
This time series segment records span of 100 days, reflecting the daily stock prices of well-known tech company. Here's an economic analysis report _ about this time series, you can refer to it. Please analyze the future trend and characteristics of this time series, and output textual descriptions with no more than 200 words.
Prompt:
Generated Text: According to the economic analysis report, the tech company faced strong market competition recently. The time series of its stock prices has been volatile. It opened $150, rose to peak of $180, then plunged to $120. Currently, it's hovering around $130. Considering the industry situation, the future trend might show slow upward recovery as the company launches new products, but short-term fluctuations are still likely due to market sentiment.
(b) Case 2
Prompt: This time series segment records span of 15 minutes, reflecting the real-time water level of a river near city. Here's a flood-warning report [ about this time series, you can refer to it. Please analyze the future trend and characteristics of this time series, and output textual descriptions with no more than 200 words.
The flood-warning report shows that there has been heavy rainfall upstream. The water level time series started at meters, then quickly rose 3.5 meters within 5 minutes. Currently, it stands at 4 meters. Given the continuous rainfall, the water level is likely to keep rising in the short term. However, if the rain stops, the water level may start to decline gradually as the river discharges water downstream, But until the rainfall situation stabilizes, the time series will remain highly volatile.
Generated Text:
(c) Case
Prompt: This time series segment records a span of 36 months, reflecting the monthly electricity consumption of a large industrial park. Here's an energy consumption assessment
about this time series, you refer to it. _ Please analyze the future trend and characteristics of this time series, and output textual descriptions with no more than 200
words.
Generated Text: The energy consumption assessment indicates that the industrial park has been gradually implementing energy-saving measures. The time series electricity consumption initially showed an upward trend, reaching high of 500,000 ,000 kWh per month. Then, after the implementation of energy saving projects, it started to decline steadily. It's now at $50,000 kWh per month. In the future, with the continuous improvement of energy-saving technologies and more efficient production processes, the electricity consumption is expected to continue to decrease gradually.
(d) Case
Figure 8: Some cases of prompt-driven textual descriptions.
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A.2 BASELINES
For zero-shot forecasting, we compare Aurora against 11 advanced foundation models: Sundial (Liu et al., 2025e), VisionTS (Chen et al., 2024a), ROSE (Wang ct al., 20251), Timer (Liu et al., 2024c), MOIRAI (Woo al., 2024), TTM (Olivares al., 2023), Chronos (Ansari et al.), TimesFM (Das et al., 2024), Time-MoR (Shi et al., 2024b), UniTS (Gao et al., 2024), and Lag-Llama (Rasul et al., 2023). We also compare Aurora with total multiple End-to-end supervised models: TimeXer (Wang et al., 2024b), PatchTST (Nie et al., 2023), iTTrnsformer (Liu et al., 2024d), TimesNet (Wu et al., 2023), GPT4MTS (Jia et al., 2024), TATS (Li et al., 2025d). CALF (Liu al., 2025b), Time- VLM (Zhong et al.), TSDiff (Kollovieh et al., 2023), TimeGrad (Rasul et al., 2021a), CSDI (Tashiro al., 2021). and GRU MAF (Rasul al., 2021b). The corresponding codebases and implementation details are summarized in Table 6.
Table 6: Code repositories for baselines.
Model Types
Models
Code Repositories
TSDiff
bttps://githbb.c amazoo sciencoluncomdiioooni series diffusion
CSDI
TimeCiad
GRU MAF
https://gituu..coo http:///uu..oooomicoooo/////ataa
GPT-MTS
bttp://pittubb.co///oooiiiae pitp MMTTS Prompe based Large Language Model-for Multimodal Time series- Forecasting
End-ooend
TATS
CALF
Time-VLM
PiccbTST
Wmnndfommer
TimeXot
https://githbbss
TimesNet
https://githbb..
Sundial
bttps:/titthub com www////mm////uneoaooeoa
VisireTS
http:// www.eeyyy///eiaae Vision
ROSE
Timer
http::/lithubb.oonttie arge- Sere--erees-Miite
MOIRAI
http:///gtthb..o httpggihhh...omomoeeeeeeeeeeeeee com/redoules//moiiii
Foundation
Chronos
http:///ithub..o
com com amazznn scienco/ forecasting
TimesFM
https///pillbbsoom wwwpgggggl--eeeeoeooee hihimesfi
Time-MoB
htlps://pillbbssoo Time- Mol///iie--Miii
Lag-Lhama
UniTS
https://ittubb.co http://ittuu..oommmmmsseese haraa///sssaaaaar/UU///Ta/
TTM
http://hhggingghae calibes ind granite dimeseries-tmm-I
A.3 BENCHMARKS
To thoroughly assess the effectiveness of Aurora, we conduct comprehensive experiments on TimeMMD (Liu et al., 2024b), TSFM-Bench (Li et al., 2025c), ProbTS (Zhang et al., 2024), TFB (Qiu et al., 2024), and EPF (Olivares et al., 2023).
For multimodal forecasting, we use Agrieulture, Climate, Economy, Energy, Environment, Health, Security, Social Good, and Traffic. For most datasets, the prediction length is set to L e {6, 8, 10, 12), while Energy and Health use L E {12, 24, 36, 48). and Environment uses L E (48, 96, 192,336).
For unimodal forecasting, we adopt ETTml. ETTm2, ETThI, ETTh2, Weather, Electricity, Traffic, Exchange, PEMS08, Solar, and Wind from ProbTS and TSFM- The prediction length is set to L E {96, 192, 336, 720), and the specific evaluation settings are different in ProbTS and TSFM- Bench.
For more short-term forecasting scenarios, we adopt datasets from EPF and TFB, the prediction lengths set as the default settings in these benchmarks.
All models are configured with the contextual length that yields the best performance as recom- mended in their respective papers. It is crucial note that, for datasets such ETTh1 and Traffic,
21

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Published as a conference paper at ICLR 2026
which are shared between "SFM-Bench and ProbTS, the evaluation settings, particularly strides, differ. A summary of the dataset statistics can be found in Table 7.
Table 7: Statistics of benchmark datasets.
Unleet
Lengtehorr
Iin 1px
Bredeek
Real
21:2
moxd of Reaal Beeker
(183
Classi
Dtought Levd
Mtualo
mudd of Eiouga Levil futeoss
1113
150 Phroe
Gavtiin Froce
2112
The phem Clodiie Xivens Proera
Air
Dudr
11.30
of Qually
Foy
ame Prangran y diods
Medlis
701
21.
The seved al thone and Reapre floar lane 1999 Peniel
Tedlic
Tianel Viduaa
531
212
TashODD
The Trard halass bitecs 1800 Prosc
ETTal
022
Toups
14:301
633
Warket
23.0%
21
21.3
TSF Seac
Recadeed fo Ba vicle your 2000 wild cesis
Triti
Trati
11.54
362
fond mics iin rncvene by 140 veme Sas Bay uce brokigs
11.9%
130
433
Whll thu use 187
NYSE
14k
1.30
21-2
serm ind aotnno rech ligh buakey
15:281
21.00
922
96
20415
Foo
Horte
14.80
073
PullS
Warker
23.00
01.
2012 S0i4
Trals
18mm
PrAtS
by
7.58
odlece
7102
Proccauv
TFR Werly
Cancuate
Vualy
1.51
173. Mudk
Uarumar Dumlets beaty la
1-1
Bostr
0.2
Usdag Gall anl 1o al Reoc Noot Peeacay Prom
21-3
New Maloaaaaaa Elcttdt Prica
and Syeee
Darakay 20x4
32.79
DC:
Prcce
1Hor
31.79
233
Long powe Arpenes
A.4 EXPERIMENTAL SETTINGS
Pretraining In the training of Aurora, we utilize Distributed Data Parallel within the PyTorch framework, as referenced in (Paszke et al., 2019). Due to the limited computational resources, all experiments are executed on only 8 NVIDIA A800 GPUs, each equipped with 80GB of GPU memory, which takes about 30 days to train Aurora from scratch. The model is optimized by the AdamW optimizer, with an initial learning rate of 5 x 10-5. To gradually decrease the learning rate throughout the training process, we implement a step decay schedule through the StepLR scheduler. The code bases described above are incorporated into the Huggingface framework. During the pre- training phase. we utilize 11 historical time series tokens and 4 prediction tokens, with a reference patch size of p 48. The batch size is configured to be 8,192.
Downstream Forecasting In the context of downstream forecasting tasks, we implement periodic patching strategies that are tailored to the temporal characteristies of each dataset. The quantity of past tokens is maintained at constant value of 11.
Furthermore, we tackle the "Drop Last" issue, which has been emphasized in recent research works (Qiu et al., 2024; 2025c; Li et al., 2025c). Specifically, when drop_last is set to True dur- ing test evaluation, may yield misleading outcomes because of incomplete batches. To uphold consistency and fairness, we configure drop_last as False for all baseline models within our exper- imental setup. In TSFM-Bench and ProbTS, all full-shot end-to-end basclines such as TimcKAN, TimePro and AMD about deterministic forecasting, and CSDI, TSDifT, and TimeGrad about prob- abilistic forecasting, follow the commonly-use 96. In EPF, all baselines follow the setting of input- 16--t-66-ou In TFB, they follow the default input lengths in short-term forecasting. The multimodal baselines such as Time VLM, CALF also follow the default settings in TimeMMD,
settings, where the input sequence length equals to
A.5 EVALUATION METRICS
With respect to evaluation metrics, in accordance with the experimental setup in TSFM-Bench and TimeMMD, for deterministic forecasting, we employ the Mean Squared Error (MSE) and Mean Ab- solute Error (MAE) evaluation metrics. In the context of probabilistic forecasting, within ProbTS, we utilize the Continuous Ranked Probability Score (CRPS) and Normalized Mean Absolute Error (NMAE). Consider the scenario featuring K variates and forecasting horizon of T.
22

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Published as a conference paper at ICLR 2026
Mean Squared Error (MSE) The Mean Squared Error (MSE) serves to quantify the average of the squared discrepancies between the predicted values and their respective ground truth values. The squaring operation within the calculation of MSE results in more substantial penalty for larger erros. This characteristic renders the MSE highly sensitive to outliers. In a formal sense, the Mean Squared Error defined as follows:
MSE 1
25)²
(26)
where K denotes the number of variables, T the prediction horizon, zf the true value, and 2f the predicted value.
Mean Absolute Error (MAE) The Mean Absolute Error computes the average magnitude of prediction errors, disregarding their direction. By concentrating the absolute differences, the MAE offers robust and interpretable metric of accuracy.
K
T 2
MAE-
(27)
KxT
where all terms adhere the same definition stated above. In contrast Mean Squared Er- ror (MSE), Mean Absolute Error (MAE) accords equal treatment to all errors and exhibits lower sensitivity substantial deviations.
Continuous Ranked Probability Score (CRPS) The CRPS evaluates the quality of probabilistic forecasts by contrasting the predicted cumulative distribution function (CDF) F' with the observed outcome The calculation is as follows:
CRPS = (F(z) /
I(x S 22)
(28)
where 1x S =} represents the indicator function. The Continuous Ranked Probability Score (CRPS) rewards distributions that assign a high probability to the true value and attains its min- imum when the predicted distribution coincides with the true distribution. In practical applica- tions, we approximate the CRPS by utilizing the empirical Cumulative Distribution Function (CDF) F(z) = thf - I(X; < z}. which is based on n = 100 samples drawn from the conditional predictive distribution po (h.)....
Normalized Mean Absolute Error (NMAF) The NMAE is an extension of the MAE. It normal- izes the MAE with respect to the total magnitude of the ground-truth values. This normalization process facilitates a fair comparison across datasets that have different scales. The formula for NMAE is as follows:
> NMAE
21=
x
(29)
Σk=l
E
x
A.6 MODEL CONFIGURATIONS
Table 8: Detailed model configurations of Aurora, including the layers of Encoder, Decoder (Trans- formers for Time Modality), Mot-hathiigg Network, TextDistller, VisionDistiller, TextGuider, Vi- sionGuider, the sizes of Prototype Bank, Model Dimension and FFN Dimension.
Model
Encoder
Decoder
Flow-Net
Model Dim
FEN Dim
Prototype Bank
Distiller
Guider
Parameters
Aurra
9
256
512
1,000
210.8M
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Published as a conference paper at ICLR 2026
A.7 EFFICIENCY ANALYSIS
Table 9: Efficiency analysis of Aurora and other baselines on Environment dataset, evaluated with the horizon of 336 and batch size of 1. We report the Parameter scale, MACs, Max GPU Memory, and Inference Time.
Models
Parameters
MACs
GPU (MB)
Inference (ms)
TATS
84.0
0.015 G
670
30.3
GPT4MTS
167.5
G.210G
1,008
61.2
CALF
211.2 M
0.724
839
44.7
Time-VLM
152.2
6.2
1,773
57.6
Sundial
128.3
G.320
586
81.3
Vision
1119
5.510
468
7.4
MOIRAL
MIL0M
G.23.G
1,280
51.0
Time Time-Mof
4
19.21 G
1,807
31.4
Autora
3
18.329
1,265
83.5
350
130
1250
150
280
Aonen Hene
Timater
Fonatty
Tamerte
Theveto
Auctry
(a) ETThl.
(b) ETTm2.
(c) Weather.
(d) Electricity.
(e) Traffic.
Figure 9: Time cost comparision (seconds) among Aurora (Zero-shot), Aurora (10% few-shot), TimeXer, iTransformer, PatchTST, and TimesNet, on datasets ETTh1, ETTm2, Weather, Electricity, and Traffic. The training and inference time are reported with batch size equals 64 in both phases.
B FULL RESULTS
Table 10: Full MASE results of zero-shot forecasting experiments on datasets from TFB. Lower values indicate better predictions. Red: the best, Blue: the 2nd best.
Tyy-
Covr Fax Feellte Madks
O Fillicr Eaite mnl Sqpenied Mtakk
Madee
Aenes
Searal
Vieee'S
Them
TowdAN
Teackker (TrecBencc
Peck TET
Condererr
Theaske
Narm
PSiteemrr
S.RAATS
RCN
LX
BF
Yeely
3.79
1441
4.7
390
47%
3349
4.7%
Quaraty
1902
1391
184
1238
1328
1.28
131
1.40
9633
2381
2807
2010
130
2136
1540
31288
1300
1328
Maddi
1:81
1000
1418
1305
1780
1.00
2060
1.36
1.80
Muss
1400
2166
1766
780
2.59
1125
X137
1:31
1875
Wendy
1839
150
348
140
138
1385
3042
1.80
45191
1821
196
1200
186
2106
3350
4577
190
1.66
1.52
139
1.00
1384
1.00
1100
1231
130
1100
31882
1.00
1390
1.66
-36
1480
1380
183
100
1.00
Hindy
2005
189
238
4.06
1318
155
3250
1.83
1.53
36781
1836
306
1882
424
5359
160
503
187
130
1.00
6350
130
113
170
330
2300
100
110
1339
2390
100
164
130
100
0100
7.66
135
Arg
3134
3.00
138
1800
133
188
3189
130
20234
2380
2650
1591
130
1623
4440
388
Table 11: Full msMAPE results of zero-shot forecasting experiments on datasets from TFB. Lower values indicate better predictions. Red: the best, Blue: the 2nd best.
Type
Zour Malk
Fudiccnt ta Sugertied Madel
Autia
1302
(323
0324
Draciornc:
(3:25
12:34
1000
(3845)
(20m
17:65
11.55
12:22
12:85
18.33
12:033
19:77
18.257
23.92
18:422
15.90
12000
19:53
18:40
18.56
11:44
2008
2831
23183
28774
31/45
22.55
21864
2.96
31453
25383
31779
3501
3522
25.07
23.22
24

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Published as a conference paper at ICLR 2026
Table 12: Full results of zero-shot & few-shot forecasting experiments on datasets from TimeMMD. Lower MSE or MAE values indicate hetter predictions. Red: the best, Blue: the 2nd best.
Typs
A Zero shot Focradaton Modzb
10% fon-abot
G Fill Malirrota Endito- Sepervised Modch
Modeb
Narccn (Durl)
(3025)
VisconTS
(2025)
(2022)
(3024)
Auroco
(0ard)
(2025)
(3315)
CALF
(2025)
Time-VU
(2025)
Matrics
MSE
MAB
MBN
MAL
MSS
MAB
MSB
MAA
MSB
MAR
MSE
MAL
MBG
MAR
MAD
MSD:
MAAE:
MAB:
Aploubue
0.154
8:242
0.335
0.205
0.18
0.319
0.304
0.364
0.266
0.333
0.162
0,342
0.391
0.137
0.100
0.289
0.161
0.207
0.257
0.288
0.140
0.285
0.215
0.287
0.265
8.297
0.565
0.425
0/433
0.907
0.348
0.406
0.855
0.202
0.625
0.36
0.310
0.230
0:366
0.244
0.300
01350
0.370
0.271
0.320
0,355
0.598
0.530
0.477
0.376
0.386
0.474
0.443
0.357
0.455
0.205
0.349
0.301
0.342
0.290
0.300
0.314
0.355
0.522
0.359
Aug
0.373
0.311
0.375
0.383
0.290
0.336
0.365
0.371
0.222
0.403
0.312
0.203
0.225
0.708
0.315
0.500
01250
0.315
0.237
0,300
8:359
1.190
0.901
1.316
0.993
1,634
1.016
0,667
1.199
0.905
1.351
0.919
1.219
0.007
0.858
0.746
1.313
0.905
1.596
1.031
2.368
1.50
0.858
0.745
1.305
0.886
1.227
0:905
1.181
0.91
0.868
0.745
1144
0.876
1.302
0.928
1.401
0.967
1.963
1.112
0.363
0.744
1.173
0.865
1,10
0.881
1.508
0.990
L179
0.580
0.875
0.753
0.370
1.297
0.925
1.414
0,957
1,929
1.101
0.509
0.749
1152
0.376
1.179
0.885
1.177
0,883
1.309
0.50
20g
0:965
0.749
1.154
0.891
1.30
0.550
1,475
0.987
1.921
1,005
0.362
0.746
1.182
0.869
1.100
0.882
1.286
0.021
1.195
0.859
B:835
0.150
0.251
0.401
0.270
0.421
0.358
0.415
0.315
11660
5.815
0.095
0.016
0.000
0.013
0.100
0.178
0.334
0.024
0.125
BJ835
0.145
11322
0/11%
0.70
01140
11:00
0.499
0/111
1055
5.15
0.099
01001
0011
01001
11:00
0.313
0107
0.111
0/833
0.143
0.304
0.443
0.307
0.446
0.256
0.432
0.432
0.528
0.816
0.101
0.015
0.704
0011
0.10
0.089
0.162
0.025
0.128
0/832
0.353
0.40
0,329
0.462
0.310
0.447
0.440
01555
0.81
0.102
0.017
0.104
0.352
0.318
0.024
0.126
0:833
0.146
0.201
0.433
0.301
0.442
0,250
0.433
0.405
0,512
0.816
0.199
0.017
0.003
0.017
0.104
0.163
0,307
0.124
0.125
0.117
0.205
1.126
0:342
0.171
0.315
11768
0.901
0.185
10:09
5.897
0.212
0.111
0.746
0.105
11112
0.714
0.114
0.555
0.226
0.555
0.555
0.345
0.254
0.395
0.365
0.669
0,250
0.396
0,199
0.333
0.232
0.362
0.216
0.204
0.10
0,346
0.227
0.359
0,291
0.40
11:18
0/59
0.345
0.454
0.001
0.367
11440
0,271
0.382
0.305
0416
03300
11:00
0.309
01410
0.383
0.400
0.413
0.515
0.352
0.431
0.545
0.456
0.000
0.675
0.255
0.500
0.22
0.367
0.334
0.420
0,479
0.336
01417
0.250
0.329
0.202
0.300
0.255
0.369
0.260
0L314
0.251
0.300
0.300
0.410
0.345
0.426
0.412
0.459
0.352
1.40.
0.309
0.372
0.315
0.400
0.307
0.390
0,313
0.382
0.304
0.357
0.354
0.363
0.50
0.413
0.370
0111
0.311
0.373
0.500
0.401
0.005
0.300
0.269
0.112
1,853
0.668
1.531
2,230
1.116
0.992
0.985
0.819
1.198
0.77
1.352
0.796
1.467
1,657
0,60
2.456
2.595
1.376
1,579
0.447
0.550
0.989
0.907
Ang
1.921
1.301
1,345
0.776
02,572
64,319
69.454
74.173
4.148
73.240
$1,503
100.000
5.19
72,433
4100
73.314
4084
74.044
93.830
753522
4312
22.502
3.264
76.573
4.238
57.003
953051
4.041
105.033
5.506
25.001
766041
4:11b
71.06
74601
4.113
60/177
4.4%%
Neg
72,475
4.004
70:441
4,005
70.500
03:245
5.173
70/003
3.085
71.457
4.088
72.400
4,007
75:376
73.731
4.151
0:781
0.561
0.43
0.057
0/530
0.966
0.522
0.689
0.427
0.11
0378
0753
0.000
0.792
0379
9:804
0.493
1196
0.549
1.106
0.208
1.312
0.85
0-284
0.06
0991
0.16
02015
0.98
0.386
0.211
0442
BJ886
0.641
1100
0.61
1.164
0.606
1.187
0.595
1.591
0.091
1.500
0.531
0.229
0.416
0991
0.00
01976
0.428
0.816
0.665
0.960
0.587
1.187
0.51
1.276
0.688
1,372
0.642
1.571
0.736
0.931
0.554
1.000
0.410
1,053
0.494
0.591
0.439
1,005
0.505
Nog
0.836
0.516
1.006
0.011
1.126
0.618
1.341
0.541
1.430
0.661
0.814
0.094
0.900
0.450
0.918
0.028
01900
0.481
0.311
0.444
6:154
0.205
0.225
9410
0225
0.411
01331
0.049
0.349
0.448
5.349
0.292
0.092
0.266
0154
0.228
0.243
0.210
01316
Tr
0.063
8.15%
0.236
0.239
0.220
11225
04463
0:555
0243
02266
0.406
0.410
1.326
0.365
0,443
0.455
0411
0.661
T1466
0.599
0.160
6.155
0.289
0.254
0.204
0.195
01257
0.216
0117
0:155
0.62
11245
11176
0.454
0.333
0.221
0.212
0126
0313
0.068
0.204
1.768
0:401
0.34
1 1.00
11312
0.458
0/400
0.165
0.796
03214
0.766
01140
012022
11:95
0.341
0.223
11523
0.064
0.289
0.221
9.455
0:241
0.400
[134]
0,451
01406
[1468
0.157
0.200
0.213
0:261
0:179
0.238
0.322
0.293
0.216
0.319
Cooor
30
23
25

---

Published as a conference paper at ICLR 2026
Table 13: Full results of zero-shot deterministic forecasting experiments on datasets from TSFM- Bench. Lower MSE or MAE values indicate better predictions. ('-') denotes datasets included in the model's pretraining and therefore excluded from testing. Red: the best, Blue: the 2nd best.
Type
S Zero shot Foundation Models
Models
Assim
Sumdid (2025)
BOSE (2025)
Timer (2024)
TimcaFM (2004)
Chronax
Timn-MEE
UniTS
MOIRAl (2004)
TTM
(3024)
Metrics
MAB
MSI
MAE
MSB
MAR
MISH
MAD
MAE
MSS
MAU
MAE
96
0.294
0.351
0.280
0.334
0.512
0.460
0.317
0.611
0.363
0.369
0.402
0.281
0.341
0.261
0.530
0.353
0.363
0.738
0.54]
E71ml
193
0.331
0.374
0.121
0.306
0,512
0.462
0.927
0.699
0.417
0.408
0.510
0.835
0.915
0.358
0.777
0.594
0.376
0.380
0.0%
0,547
896
0.359
0.191
0.350
0.389
0.523
0.470
1.943
0.704
0.447
0.428
0.550
0.477
0.969
0.395
0.754
0.590
0.999
0.905
0.600
0.533
720
0.414
0.394
0.418
0.553
0,410
1.044
0.723
0.513
9.509
0.432
0.417
0.600
0.500
A0g
0.383
0.336
0.377
0.525
0.471
0.958
0.674
0.435
0.761
0.543
0.300
0.389
0.692
0,543
96
0.170
0.270
0.170
0.256
0.234
0.309
0:325
0.500
0.205
0.267
0.192
0.249
0.315
0.189
0.260
0.216
0.309
E77ma2
193
1.213
0.317
0.229
11300
0.266
0.333
0.280
0,339
0.243
0.331
0.256
0.30%
0.225
0.312
11309
0.353
0.247
0.300
0.311
0,300
196
0.157
0.781
0.517
0.510
0.188
0.335
0.559
0.411
0.414
0.315
0.546
0.291
0.348
0.355
0.581
0.705
0.334
0.350
0.183
720
0.300
0.251
0.357
0,355
0.417
0.414
0.46
0.475
0.430
0.431
0.372
0.256
0.446
0.435
Aug
0.324
0.258
0.330
0.299
0.352
0.315
0.36
0.347
0.300
0.335
0.371
0.276
0.330
0.333
0.372
96
1.340
0.161
0.148
0.855
0.833
0.408
0.454
0.444
0.421
0.401
0.444
0.400
0.549
0.379
0.317
0.992
0.100
0.908
0.954
0.199
ETT3
192
0.577
0.415
0,395
0.418
1,400
0.420
0.522
0.405
0.472
0,432
0.500
0.443
0.315
0.413
0.398
0.421
0.440
01444
1.356
0.417
356
11.90
0.422
01423
01440
11004
0.426
0.358
0.684
0.51
0.455
01550
0.400
0,455
0.414
0.904
0.422
720
0.428
0.450
0,481
0.893
0.430
0.447
0.714
0.549
0.514
0.481
0.459
0.463
0,705
0.568
0.624
0,448
0.386
0,415
11411
0.634
0,402
0.425
0.562
0.483
0.479
0,442
0.412
0.427
0.414
0.425
0,510
0.469
0.395
0.417
96
0.259
0.225
0.271
0.233
0.295
0,362
0.316
0.359
0.325
0.355
0.306
0.335
0.292
0.352
0.323
0.385
0.287
0.225
0.277
0.335
ETTR
192
0.334
0.133
0.220
0.76
0,36
0.385
0.314
0.398
0,397
0,400
0.396
0.394
0.347
0.379
0,372
0.485
0.347
0.367
0.334
0,373
356
1.400
0.35
0.000
0.355
0.399
0.381
0.410
0.431
0,413
0.423
0,406
0,395
01302
0.402
720
0.403
0,441
0,281
0.435
0.305
0.432
0.400
0.434
0.445
0.444
0.442
0.457
0.404
0.431
0.408
0.444
Axg
14.312
0..144
0.33
0.387
0.546
0.395
0.370
0.400
0.400
0,400
0.390
01WW7
0.321
0.399
0.314
0.408
0.354
0.377
0.345
0.989
96
0.160
0.207
0.157
0.286
0.200
0.260
0.100
0.236
0.166
0.308
0.157
0.211
0.194
0.234
0.177
0.208
0.153
0.242
Weates
192
0.382
0206
0251
0.299
0.288
0.261
0.293
0,238
0.255
0.308
0.256
0.252
0.279
0.219
0.240
0.229
0.285
336
0.288
0255
0340
0.279
0.315
0.332
0.540
0.292
0.259
0,330
720
0.307
0,227
0.320
0.33
0.340
0.357
0.365
0.381
0.466
0.361
0.365
0.350
0,359
0.370
Ang
1.2300
0,207
0.205
0,315
0.292
0.313
0.268
0.309
0.256
0.209
0.275
0.245
0.200
01275
0.208
0,307
Bleanay
192
96
0.161
0.134
0.284
0.25
0,830
0.552
0.229
0.500
0.209
0219
0.307
0.315
0.210
0.250
0.312
0.337
0.155
0.178
0.269
0.273
0.152
0.171
0.242
0.259
0.166
0.191
0.263
0.286
336
0.287
0.173
0.511
0,236
0.254
0.372
0.287
0.278
0.237
0,336
720
0.320
0,218
0,311
0.275
0.328
0.456
0.479
0.248
0.335
0.236
0.313
0.292
0.384
Asy
1.178
0.275
0.100
0.265
0.234
0.320
0.297
0.375
0.198
0.291
0.188
0.203
01212
0.317
96
1.435
0,214
0.802
0.407
0.336
0.566
0.563
0.378
D.514
0.347
TraTee
192
0.455
0328
0.575
0,406
0.561
0.385
0.579
0.412
0.545
0.373
0886
0.64
720
0,670
0,411
0.618
0.422
0.549
0.464
0.733
0.472
0.622
0.433
Asg
0.524
0,353
0.335
0,122
0.613
0.407
0.615
0.421
0.564
0.385
96
1.188
0.222
1.216
1.230
0.534
0.587
0.591
0.904
0.408
0.348
0.373
0.494
0.104
0.345
0.711
0.594
0.681
0.688
D8858
0.564
Sola
192
8.118
0251
02346
0.007
0.550
0.680
0.567
0.465
0.373
0.363
0.303
0.309
0.342
0.800
0.618
0.694
0.665
0.833
0.695
11111
11260
1,00
0.583
0:831
0.606
0.525
0.719
07200
0885
0.741
720
12218
0.107
0.241
0.272
0.499
0.594
0.072
0.70
0.601
0.461
0.444
0.540
0.376
0.791
0.790
0.218
0.718
0.794
Asg
0.203
0.289
0,221
0.252
0,505
0,549
0.771
0.004
0.500
0,397
0.393
0.319
0,413
0.429
0.345
0.569
0.714
0.204
0,815
0.710
96
1.665
0,513
1,375
0.995
0.500
1.131
0.750
1.338
0.961
1.152
0.833
1.264
0.684
PEMS00
192
(1590
0,577
1.965
0.979
0.768
[1661
1.609
0.944
1.719
0.983
1.259
0.675
1.638
1.090
356
1.500
9,546
1,338
0.900
091
0.716
1.505
1.768
1979
1,300
720
1.629
0,570
1.401
0.900
1131
0.014
1.633
0.986
1,800
1.191
1.200
0.905
2,000
1.175
Asg
0.563
0,552
1.369
0.979
0.366
0.995
1.485
0.907
1.507
1.004
1.253
0.579
1.730
1.065
96
0.061
0,851
11646
1.072
0,747
0.960
0.659
1.229
0,022
1,273
0.733
1.112
0.724
0.991
11.56
1.177
0.701
192
1.188
11.66
1.509
0.904
1.142
0.758
1.503
0.838
1.49
0.817
1.285
0.606
1.221
0.566
1.350
0.625
336
1239
0.799
1,318
0.848
1.300
0.30
1.739
1,550
3.509
1526
0,890
1,403
0344
1473
0.891
720
1,309
0,840
10000
1.004
0.981
1.417
01864
1.963
0.997
1/69
01914
1,765
0.913
1.581
0915
1.447
0.999
Aag
1.151
0,763
1.186
0.772
1.251
0.820
1.201
0.753
1.613
0.870
1.478
0.834
1.425
0.848
1,200
0.705
1.337
0.829
24
1.203
0,212
0.370
0.393
0.383
0.009
0,235
0,332
0.450
0.549
0.755
0.623
36
0.417
11566
0,778
0161
0.472
01,67
01912
0.761
46
0,605
0,574
1,043
0.729
1.300
0.792
0.721
0.591
1353
0.579
60
0.320
0,681
1.425
01880
1.594
1.000
1.001
0,763
1.754
0.955
1.712
1,016
Axg
0.528
0,526
0.590
0.642
0.988
0.704
0.623
0.536
1.129
0.730
1.230
0.620
1" Counc
27
21
11
12
3
0
5
0
0
26

---

Published as a conference paper at ICLR 2026
Table 14: Full results of zero-shot probabilistic forecasting experiments on datasets from ProbTS. better predictions. (-") denotes datasets included in the model's pretraining and therefore excluded from testing. ('") denotes the excessive time consump- tion. Red: the best, Blue: the 2nd best.
Lower CRPS or NMAE values indicate
1yye
Zeno-chot Foundation Models
8 Hull-shor Profubiliaic Radhat-ed Superviso Modals
Models
Asrom
Sundial (2025)
Chroces (2024)
MORKAI
Lag Lamma
ISD
CSD1
TimeCrra
GRU MAP
(Ous)
(2024)
(2023)
(2023)
(2022)
(2022)
(2621)
Metrics
CRPS
NMAE
CRPS
NMAE
CRPS
XMAA
CRPS
NMAE
CRPS
NMAE
CRPS
NMAE
CRPS
XMAE
CRPS
NMAA
CRPS
NMAE
96
01261
0.277
0.93%
0.100
0.422
0.454
0.354
0.400
0.346
0.441
0.30%
0.522
0.645
0.255
316
0.369
0.30
0425
0.534
0.357
0.416
0.571
0.419
0.501
0.790
0.439
720
0.312
0.330
0.318
0.500
0.461
0.478
0.514
0.535
0.403
0.466
0.478
0.622
0.448
0.578
0.621
0.703
0.536
0.711
A11g
117445
0,385
0.385
1.311
0.413
0.451
0.492
0.536
0.378
0.430
0.007
0.519
0.324
0.421
0.587
0.736
0.412
0.524
145
11:31
0.148
0.128
0.153
0.134
0.158
0,176
0.186
0.163
0.392
0.175
0.224
0.115
0.146
0.427
0.525
0.177
0.312
0.197
0.181
0.316
0.152
3.167
0.195
0.100
0.204
0.229
0.206
0.229
0.325
0.397
0.248
0.409
0.566
0.377
73U
11182
0.201
11189
0212
0.223
0.230
0.321
0.258
0:217
0.349
0.344
0.416
0.239
0.306
0.470
0.561
0.72
0.355
11.55
0.175
0.158
1.166
0.178
0.194
0.251
0.220
0.194
0.219
0.276
0.338
0.173
0.212
0.448
0.565
0.309
0.377
96
1.272
0.316
0.360
1.307
0:091
0.541
0.460
0.484
0.207
0.340
0.985
0.510
0.437
0.557
0.485
0.58
0.205
0.171
ETT1
192
0.297
0.356
0.203
0347
0348
0.384
0.406
0.492
0.312
0.356
0.467
0.306
0.495
0.625
0.516
0.690
0.348
0.430
03655
0.508
0.326
0.370
0.574
0.512
720
0.351
0.312
0,366
04113
0.425
0.526
0.340
0.415
0.516
0.657
0.657
0.523
0.672
0.393
Aug
11
0.342
11346
0:360
0.940
0.488
0.494
0.319
0.368
0.457
0.536
0.429
0.603
0.500
0.691
0.355
11144%
0.174
01157
11155
0.163
0.191
0.212
0.254
0.178
0.70M
0.335
0.421
0.164
01214
0.358
0.645
0.259
0.297
ETTT
192
11166
0.195
0118
1213
0.195
0.218
0.229
0.250
0.198
0.217
0.265
0.339
0.225
0.294
0.457
0.55
0.313
0.376
0.212
0232
0.226
0.341
0.255
0.211
0.427
01355
0.376
0.714
0.779
0.233
0.487
01302
0.205
1116
0.210
0.225
0.245
0.201
0.417
01311
0.435
00480
96
0,064
0.070
0.075
0.091
0.136
0.185
0.061
0.055
0.004
0.104
0.113
0.063
0.097
0.100
0.164
0.139
192
0.000
0.075
0.083
0.097
0.145
0.165
0.173
0.139
0.994
0.002
0.134
0.144
0.065
0.015
0.127
0.159
0.143
0.166
336
0.077
0.090
0.106
0.136
0.151
0,149
0.134
0.98
0,309
0.137
0.138
0.083
00095
0.130
0.102
0.129
0.168
730
0.851
0.113
0.235
0.107
0.120
0.122
Avg
0.102
0,142
0.179
0.134
0.003
0.138
1.083
01203
0.05
Bleansly
192
11221
0.074
0.256
0,279
0.345
0.441
0,200
0.264
0.300
0.124
0195
0.120
376
1.106
0.003
IL100
0.365
0.789
0.962
0.571
0.100
0.126
0099
0.129
730
0.181
0.134
0.098
IL1ER
0,274
0,318
0.478
0.632
0.108
0.134
0.106
0.1%6
Aug
0.085
0.10%
0.001
IUNK
0.347
0.790
0.907
0.519
0.100
0.136
0.084
0.122
0.241
0.250
0.289
0.255
0.342
0.214
0.222
0.215
Tallic
336
192
12225
1.207
0.269
0.250
0369
0249
16290
0.27
0335
0295
0.364
0.343
0317
0206
0.392
0.354
0:224
2223
1227
0.59
770
11250
0.749
6:300
03321
0434
0.517
03911
0.478
0.353
13374
0.230
0.262
02209
0.205
0.300
0.365
0.327
0.392
0.225
0.264
96
0.124
0.026
0.024
0.027
0.021
0.025
0.025
0.000
0.42
0.051
0.079
0.000
0.028
1.036
0.068
0.079
0.026
0.083
Euchange
192
1036
0.34
00047
0.106
1058
0034
316
RJ44
0.44
0046
00000
0061
0.04
0.677
6903
0.61
0.073
0.81
0.10m
0.000
0.06
0.004
0086
0008
0.074
720
0.075
0.078
0.076
0.79
0.078
0.080
0.073
0.061
0.078
0.004
0.082
0.142
0.143
0.175
0.099
0.113
0.160
0.162
Avg
IL144
0.347
0.045
0.949
0.044
0.047
0.045
0.050
0.037
0.069
0.084
0.111
0.069
0.085
0.062
0.003
0.070
0.083
24
1.110
0.128
0.305
0.123
0.130
0.139
0,150
0.196
0.135
0,113
0.225
0.242
0.250
0.263
0.275
0.296
0.231
0.375
11169
03315
0.111
0.165
0.346
01298
0.244
0242
0.146
1144
0.386
0.351
0.171
0.335
01901
0:394
0.780
1.156
0.177
0.165
0.100
0.996
0.224
0.663
0.184
0.156
0.311
0.263
0.232
0.381
0.209
0.395
0.505
0.285
0.114
0.147
0.166
0.166
0.170
0.197
0.159
0.197
0.156
0,211
0.248
0.259
0.276
0.250
0.284
0.300
0.262
0.288
1" C'Cuu
24
8
0
0
27

---

Published as a conference paper at ICLR 2026
Table 15: Full results of zero-shot Aurora v.s full-shot end-to-end supervised models. Lower MSE or MAE values indicate better predictions. Red: the best, Blue: the 2nd best.
Models
Aurors (Ours)
TimeKAN (2025)
AMD (2025)
TimePro (2025)
TimeXer (2024)
Fredfoemer (2024)
iTannformer (2024)
PatchTST (2023)
TimesNet (2023)
Mettic
MSG
MAE
MSC
MAE
MSB
MAG
MSC
MAG
MSE
MAE
MSE
MAE
MSE
MAD
MSE
MAD
MSE
MAC
95
0.294
0.351
0.327
0.365
0.327
0.361
0.326
0.364
0.309
0,351
0.326
0.361
0.334
0.368
0.329
0.367
0.338
0.375
677ml
0.331
0.374
0.363
0.387
0.366
0.983
0.567
0.583
0.355
0.378
0.363
0.384
0.377
0.367
0.385
0.374
0.387
336
0,339
0.391
0.389
0.417
0.398
0.404
11402
11409
0.387
0.399
0.315
0.416
0.426
0.420
0.399
0.410
0.410
0.411
0.414
0.457
0.445
0.464
0.437
0.460
0.446
0.448
0.435
0.456
0.441
0.491
0.459
0.454
0.430
0.478
0.490
Avg
0.346
0.383
0.384
0.401
0.389
0.296
0,391
0
0.375
0.391
0.385
0.398
0.407
0.410
0.357
0,800
0.400
0.405
0.270
0.262
0.176
0.178
0.171
0.177
0.258
0.180
0.175
0.267
ETTm2
192
0.232
0,307
0.244
0.308
0.242
0.002
0.242
0.500
0.238
0.300
0.245
0.301
0.250
0.309
0.241
0.300
0.249
0.309
0.337
0.305
0,346
0.298
0.237
0.303
0.342
0.301
0.302
0.311
0.305
0.321
720
0,338
0.350
0.402
0.404
0.396
0.796
0:400
0.399
0.401
0.397
0:404
0.398
0.412
0.407
0,402
0,400
0.408
0.403
Avg
0.356
0.304
0,350
0.323
0.306
0.278
0.321
0.258
0.281
0.306
0.291
0.333
96
0.340
0.381
0.374
0.391
0.375
0.792
0.375
0.308
0.377
0.397
0.378
0.395
0.386
0.405
0.414
0.419
0.384
0.402
ETTAL
0.405
0.421
0.429
0.425
0.455
0.441
0.460
0.436
0.429
336
0.399
0.422
0.464
0.440
0.471
0.443
0,472
0.400
0.487
0.443
0.485
0.447
0.487
0.43%
0.501
0,466
0.491
0.469
0.450
0.462
0.464
0.476
0:464
0.463
0.496
0.503
0.500
0.521
Avg
0.386
0.415
0.431
0.429
0.438
0.430
0:438
0.438
0.431
0.432
0.488
0.435
0.454
0.448
0.459
0.455
0.458
0.450
0,259
0.325
0.293
0,343
0.287
0.338
0.290
0.345
0.289
0.340
0.291
0.342
0.297
0.349
0,302
0.348
0,360
0.374
BTTh2
192
0.324
0.370
0,375
0.346
0.367
0.288
0.367
1.394
0,570
0.391
0.372
0.390
0.380
0.400
0.388
0,800
0.402
0.414
336
0.360
0.401
0.429
0,441
0:110
0.424
0:419
0.431
0.422
0.434
0.119
0.431
0.428
0.432
0,426
0.433
0.452
0.452
720
0.403
0.441
0.466
0.468
0:421
0.440
0.427
0.445
0:429
0.445
0.431
0.450
0.427
0.445
0.431
0.446
0.462
0.468
0.337
0.384
0.391
0.412
0.371
0.397
0.377
0.404
0.378
0:403
0.378
0.403
0.383
0.407
0.387
0,407
0.414
0.427
0.160
0.207
0.164
0.210
0.174
0.221
0.166
0.207
0.168
0.200
0.163
0.207
0.174
0.314
0.177
0.218
0.172
0,220
Weuther
192
0.302
0.247
0.209
0.250
0.219
0.259
0.216
1.254
0.220
0.254
0.224
0.258
0.221
0.254
0.255
0.259
0.219
0.261
0.288
0,264
0,240
0.273
0.296
0.273
11296
0.276
0.296
0.278
0.298
0.278
0.278
0.297
0.280
0.305
720
0.307
0.327
0.343
0.342
0.349
0.345
0.351
0.346
0.353
0.347
0.357
0.350
0.358
0.349
0.354
0.348
0.365
0.359
Avg
0.267
0.245
0,273
0:254
0.280
0:252
0,276
0.254
0.256
0.258
0.266
0.259
96
0.134
0.234
0.174
0.266
0.147
0.251
0.139
0.234
0.151
0.247
0.148
0.242
0.148
0.240
0.195
0.285
0.168
0.272
Elestniily
192
0.161
0.258
0,182
0.273
0.176
0.262
0.156
0,249
0.165
0.261
0.105
0.257
0.162
0.253
0.199
0.184
336
0.190
0.287
0.197
0.286
0.193
0.281
0.172
0.267
0.183
0.280
0.180
0.274
0.178
0.269
0,215
0.305
0.198
0.300
0.234
0.330
0.236
0.920
0.232
0.320
0.220
0.188
0.235
0.256
0.220
0.320
Avg
0.178
0.275
0.197
0.286
0.187
0.781
0.169
0.262
0.180
0.274
0.178
0.220
0.178
0.270
0.216
0.304
0.193
0.295
0.314
0.423
0.286
0.443
0.208
0:426
0.202
0.416
0.403
0.395
0.544
0.359
0.593
0.321
Tratie
192
0,405
0.324
0.442
0.295
0.496
0.323
0.439
11.99
0.435
0.21%
0.429
0.289
0.417
0.276
0.540
0.354
0.617
0.336
0,525
0.355
0,473
0.335
0.520
0.330
0.449
0.307
0.451
0.296
0.441
0.433
0.283
0.551
0.358
0.629
0.336
720
0,670
0.431
0.481
0.357
0.540
0.344
0.475
11,009
0.484
0.514
0.463
0.300
01.667
0. 0.302
0.586
0.375
0.640
0.350
0,524
0,352
0.455
0,318
0.500
0.324
0.447
0.302
0.447
0.295
0.434
0.289
0.428
0.282
0.555
0,362
0.620
0.336
Coun:
18
0
3
1
1
3
0
28

---

Published as a conference paper at ICLR 2026
Table 16: Studies Aurora (zero-shot) and Aurora (10% few shot) v.s few-shot (10% and 20%)
end-to-end small models, i.e., PatchTST, iTransformer, TimesNet, DLinear. Red: the best, Blue: the 2nd best.
Moska
Ausuea
Austra (8C%;
PaacITTT (19%)
Tinasfoua (19R)
(105)
DLisar (199)
PaadESST (299)
Thansferaa (259)
Tares (209)
DLaser (209)
Markc
XSSB
MAA
MSE
MAE
MSS
MAE
KSS
MAA
M158
MAB
3458
MAE
MSS
M4B
MSS
MAE
MSS
MAB
3458
MAE
0.741
1063
0.856
11661
01900
1001
1,455
8.85%
0.745
1008
1.53
0978
1831
1.455
13324
0.744
11915
0.865
1935
1:79
11945
1.38
03315
01744
1400
1.00
1.105
0.597
13555
19981
1,422
13325
0.749
01744
20155
01935
1.500
1.52
1.00
1333
19315
1.534
1809
1,422
0,005
0.150
10115
0.855
0.204
0.381
01:15
3.266
0.805
0.50
0.315
0105
0.350
0.356
01132
3865
11375
0.174
16330
0.003
0.115
10115
10819
0.52
01330
01:33
3.266
14437
6417
06533
0161
11361
13033
0156
3390
11315
0.136
14307
0.002
0.345
1035
18881
0.224
0.392
01:144
03291
33599
0.555
0425
03331
0101
11342
02544
0145
0398
11334
01145
1L315
0.002
0.144
0035
10882
0.347
0.402
0.379
3.288
04430
0499
0.335
0107
0.335
02400
0166
0365
01334
01172
(L34)
Arg
0.003
0.166
00056
00899
0.352
0.350
0.319
10399
3.275
0.437
0.323
0.597
0.06
0.352
0.09
0145
0347
0,237
0,15
00325
0.117
0.45
0037
0222
0.611
01548
01355
0692
0003
1.147
0339
0133
090
0600
06222
0.00
04544
0.54
0129
0222
1.811
0.968
1000
1,337
1013
0.343
1299
0006
1127
0221
1:23
0.204
0.000
0221
0220
3.012
1,353
1,72
1.568
1554
1907
120
1955
0.383
4.59
2.554
1836
1.533
2599
1064
200
1958
1,630
Avg
0.205
0.29
2.666
1.545
1234
1555
0.943
1433
080
1.40
0.59
1277
0355
03518
D-108
3.411
0.503
0.787
0.31
0.457
010
0.51
0.757
465
0.581
0355
01553
0.503
0.91
01313
0463
0901
01565
0.787
192
0.210
0.59
0.369
1354
01567
01567
2.536
3:511
0.59
0.525
06752
0.500
0363
0556
16507
0.912
336
0.200
1355
11.93
0.596
0.523
0.534
06767
0.516
0.533
03512
0510
0.50
15533
0.554
Arg
0.11
01553
0.554
01562
3.564
0.53
0.112
0.342
0.485
0.494
0.45
D/65
0310
06422
0.901
11503
0.666
19641
2449
1:284
2:131
1055
2.555
1.000
2.172
1.000
2.139
1.288
1.566
DIMS
1389
11977
3.31
1.115
1.512
1359
1.312
1335
2,02
1.205
24400
1
3.346
1.238
2:265
11995
2.30
1.255
2467
1990
280
11300
2,547
1.354
1.669
1100
1447
1055
3.177
1.340
2348
1.75
1.55
1.435
2440
1.222
2351
1.001
2445
1077
250
1.091
2.411
1.225
1883
0160
1.59
10547
3.158
1.258
3.112
1.300
3.2%4
1.291
2:757
1.332
2727
1.800
2359
1150
1006
1877
2.366
1.395
Avg
1.553
0.500
1349
0076
27811
1.167
23555
1.211
3.145
1.259
2,403
1.335
2,400
1
2354
1070
2.50
1026
2,466
1.225
61.072
44:13
3229
15:331
5.200
75.251
11.213
4.62
14.28
4.71
75.285
5023
76.01
Sautty
70.776
1103
3520
10.33
5,320
90.259
16.23
4.92
71.355
3.15
19.25
16177
13:945
54923
5.311
75.355
5332
$4.25
13.521
56.295
5025
50.285
5425
97.250
5,58
4.70
77.78)
5334
31.70
0.701
0.615
11427
10:313
(048)
5.199
1.511
5090
1.517
1905
01541
3.91
D677
197
11565
1445
11551
0.663
11,077
1.555
5455
1.332
0.550
2550
6347
2476
11500
3.275
11533
11.515
1.475
0.510
1.111
1.228
5.555
1.254
2354
0.375
2555
0753
2351
10790
2.39)
0.500
0.554
13.642
2.315
1.244
1332
0.305
1.312
2550
0.750
3.33
0256
2427
15533
2.59)
0316
0.454
11,022
1.500
0,412
1142
5.534
1.300
5631
1.397
2.53
0.31
2513
0257
2200
2,277
0.793
0.154
0.305
11232
2301
1.505
2317
1/36
2,086
1.335
2.740
1.319
2.154
1.304
13333
D052
1.766
11559
2.544
1.305
0.118
0.336
0.555
16384
2,890
1.583
2.166
1/9
2501
1.334
2014
1.234
1.04
0.922
1334
0516
2.23)
1.303
2.145
1.119
0.163
0.200
0.000
0.387
2507
1.180
2.143
1996
2.799
1.308
2.336
1.20
2.140
1.505
1.43
0994
2.465
10935
2,121
1.804
0.168
0.256
0166
0.235
2864
1.115
2.39
1,000
3,890
1.239
2/627
1.344
1,955
0.955
1.199
0543
2:200
1.822
2.176
1.127
Arg
0.161
0.380
0.597
11290
2815
1.12]
2.59
1,02
3521
1.352
2,642
1,25
2/466
0.997
1.505
0.546
2.148
1.077
2,197
1,39
29

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Published as a conference paper at ICLR 2026
C
SHOWCASES
SHOWCASES OF DATASETS WITH SIMILAR HISTORIES BUT DISTINCT FUTURES
Time
(a) The visualization of dataset tourism_ prrrissmmmothlyy... dataset 275 in TFB.
2.0
-2.
-1.
156
201
206
100
(b) The visualization of dataset m4_ monthly dataset 1456 in TFB.
Figure 10: Visual comparisons between datasets tourism_monthly monthly dataset_ 275 and m4_ mo4_monthly__mm Soonthly_datase____ 14569 from distinct domains. Blue part indicates the historical similar time series, and purple part indicates the distinct future horizons.
Time
(a) The visualization of dataset m4 m4__mm__aaaaaa yearly dataset 1639 in TFB.
(b) The visualization of dataset finance 87 in TFB.
Figure 11: Visual comparisons between datasets Searrl_dataset_1666 1639 and finance 87 from dis- tinct domains. Blue part indicates the historical similar time series, and purple part indicates the distinct future horizons.
30

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Published as a conference paper at ICLR 2026
55 (a) The visualization of dataset cif 2016 dataset 10 in TFB.
Time
The visualization of dataset cif___22000000 2016 dataset_ in TFB.
Figure 12: Visual comparisons between datasets cif 2016 l_doddaet______ee 10 and cif 2016 dataset_ 8 from the same domains. Blue part indicates the historical similar time series, and purple part indicates the distinct future horizons.
2016
1000
2003
2003
2000
1000
4020
(a) The visualization of dataset bitcoin, datoooit_datttttt_te wtttddaatttttttttttttmo _ values_llee in TFB.
200
2503
2500
Time
(b) The visualization of dataset bitcoin satpidddaaattttbhaaaee taiistmmmmssslssesssssssssssss 12 TFB.
Figure 13: Visual comparisons between datasets bitcoin dataset_without without missing values 14 and wettautttttthutttttteeteoo
waluess bitcoin dataset without sisroouttmiiiing__tm values_ 12 from the same domain. Blue part indicates the histori- cal similar time series, and purple part indicates the distinct future horizons.
31

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Published as a conference paper at ICLR 2026
C.2 VISUALIZATION OF THE PROTOTYPEBANK
WVA
M www
1m
AM
MAA
MA
NAY
M
MAA
M
AN
MM
A
M
WI
WN
MA
WW
WA
MV
MA
WI
NV
UW
MMM
WV
Harpyp
WM
www
W
MW
WA
M
MN
M
WV
MAN
W
Figure 14: The visualization all 1,000 prototypes in PrototypeBank. Note that though some prototypes may look similar due to drawing, they actually differ in their magnitutdes and phases.
32

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Published as a conference paper at ICLR 2026
C.3 VISUALIZATION OF GENERATED PROTOTYPES FOR PREDICTIONS
Groundruth
1111110
Predccion
Confidence Isterva
50% trtenval
300
240
200
375
110
200
4003
600
Tlmee
Electricity
ETTh1
26-
1 Mmmnaaa w
15-
Croundtruth Prndiction
Groundmmch
Predict
50% Confidence interval
-15
Confidence Interval
220
250
400
630
520
2020
2002
Time
(c) ETTh2
(d) ETTml
M
Groundouth
Preditiin
80% Cont dence Interval
10
Time
Time
(c) ETTm2
(1) PEMS08
Cro unduudi zth
Groundrusth
Predictos
Contidence Intevvl
0.4
0.2
00
Time
Time
(g) Solar
(h) Traffic
1515
Gmundtmeh
1030
1030
Predict on
1925
1585
Corfidence interva
1005
3
Groundinth
Prodiction
50% Coriiiionce intova
475
100
1000
1:50
2000
2500
9500
são
1600
1550
2003
Weather
Wind
Figure 15: Visualization of TSFM-Bench
33

---

Published as a conference paper at ICLR 2026
Graundzzth
Confidance
3460
2404
Groundrcch
Frektenn
15.2
mnS Corfilence inme
Time
(a) economics 68
(b) finance 46
Gro
1460
Groundtruth
Prodictio
Prediction
Confidence Interval
Confidence Interval
860
260
Time
human_33
cwp_e008_______ (d) _ud_cpd___p__ cup 2018 dataset without wwthowtttssiitntggoo.o.mc.e values_20 20
4200
4400
on
Grundmuch
Corfidencce Intevvs
15523
15327
Groundrruth
3400
13503
2400
13323
2200
Timm
Time
(e) m30 ____uurterly___c dataset_3a
(f) m4 _dil_ dataset 3397 dat9
320
600-
17.2
17.0
5:0
11.8
40
30
11.0
23
60
(g) mou_lo ddtlly__ttt_____ 24
(h) nature 68
Piedicton
90%
J3302
Croundiruth
Pediciias
10303
60% Confilence Interval
60
20
40
(i) tourism monthly dataset 297
i vehicle trips dataset without missing values 131
Figure 16: Visualization of Univariate Datasets in TFB
34

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Published as a conference paper at ICLR 2026
Prediction
Canfidence
Groundr
Prediction
0.2
80% Contidence Inteval
25
1.5
150
375
230
53
15
100
125
150
175
270
(a) Agriculture
(b) Climate
3.0
Goundr--th dh
Pred etion
80% Contidece Interce
AM
Broundruuh
2.5
Pradiction
3.0
80% confidunce Interva
100
125
150
175
200
200
4.0
300
1000
(c) Economy
(d) Energy
Groundrumh
30
Groundtruth
Prediction
dence
Contidence Interval
20
60
00
100
52
120
150
200
(e) Health
(f) Security
3
Groundtruth
2.5
Groundiruth
Prediction
2.00
Fred cbo
Interval
80% Coniidence Interoat
1.25
0.50
3
53
200
125
150
375
250
22
42
300
SocialGood
(h) Traffic
Figure 17: Visualization of TimeMMD
35

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Published as a conference paper at ICLR 2026
C.4
VISUALIZATION OF MODALITY- GUIDDE ATTENTION WEIGHTS
Modality
Coldence
1.5
Groundtruth
1.0
Prediction w/o Modality Guidance
Prodiction Modality Guidance
0.5
80% Confidence Interval
80% sence
6113
0.0
100
125
150
200
032
5313
0.50
0.01
0:03
c.0
03
0.100
Time
(a) Predictions Agriculture, without Modality Guidance v.s with Modality Guidance. With Patch Size equals 48 (Separated by black dashed lines the figure).
(b) The visualization of attention weights, without Modality Guidance with Modality Guidance.
Figure 18: It is observed that the predictions with modality guidance are more accurate. The 4 patches (TI - TT4 of the contextual time series show similar correlations without modality guid- ance. While with the modality guidance, the correlations between T1 and T2 are further focused on, because their correlations are similar to the T4 and future values, with a trend first decreasing and then increasing. Additionally, the correlations between T4 and Tl are also focused on for prediction.
Groundtruth
Prediction Modality
2
Prediction Modality Guidance
Confidence
Model Saldance
Mudi:
Guddence
80% Confidence Interval
0318
0J38
0.220
0.277
6.22
CAI
$.200
0
9.09
4.07
6.71
6:21
CAR
0.1
0.00
0.00
0.477
Time
(a) Predictions Climate, without Modality Guidance with Modality Guidance. With Patch Size equals 48 (Separated by black dashed lines the figure).
(b) The visualization of attention weights, without Modality Guidance v.s with Modality Guidance.
Figure 19: It is observed that the predictions with modality guidance patches (T1- - -T4) of the contextual time series show similar correlations without modality guid- ance. While with the modality guidance, the correlations between T3 and T4 further focused on, because their correlations are similar to the T4 and future values, simply copying the trend can lead higher accuracy.
accurate. The
2-
Modallly
duliaance
Groundtruth
24
Prediction Modality
0,19
8313
0:200
8:307
Prediction Modality Guidance
5.75
6.20
C442
0232
Confidence interval
80% Confidence interval
100
125
150
175
Time
(a) Predictions on Economy, without Modality Guidance with Modality Guidance. With Patch Size equals 48 (Separated by black dashed lines in the figure).
(b) The visualization of attention weights, without Modality Guidance v.s with Modality Guidance.
Figure 20: It is observed that the predictions with modality guidance are more accurate. The patches (T1 - T44 of the contextual time series show similar correlations without modality guid- ance. While with the modality guidance, the correlations between T3 and T4 are further focused on, because their correlations are similar to the T4 and future values, simply copying the trend can lead to higher accuracy.
36

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Published as a conference paper at ICLR 2026
Groundrruth
Prediction wio Modality Gui Guidence
Prediction w Modality Guidance
Value
80% Confidence Interval
mmm
80% Confidence Interval
25
100
125
1.50
1755
200
Time
(a) Predictions on SocialGood, without Modality Guidance v.s with Modality Guidance. With Patch Size equals 48 (Separated by black dashed lines in the figure).
(b) The visualization of attention weights, without Modality Guidance v.s. with Modality Guidance.
Figure 21: It is observed that the predictions with modality guidance are more accurate. The patches (T1 T3) of the contextual time series show similar correlations without modality guidance, and are highly dissimiliar to T4. While with the modality guidance, the correlations between T4 and T2 are further focused on, because their trends share potential similarity, first decreasing then increasing. And the correlations between T2 and T3 are also focused, which are similar to the correlations between T4 and future values.
D MORE MODEL ANALYTICS
Table 17: The studies on different sizes of the PrototypeBank.
Models
1000
5000
Metries
MSE
MAE
MSF
MAF
MSE
MAE
Agriculture
0.288
0.364
0.272
0.348
0.269
0.344
Climate
0.893
0.760
0.865
0.749
0.863
0.752
Economy
0.034
0.152
0.033
0.146
0.032
0.144
Energy 0.271
0.391
0.255
0.370
0.260
0.373
Environment
0.302
0.413
0.276
0.379
0.268
0.372
Health 1.587
0.866
1.553
0.850
1.561
0.854
Security
75.017
4.118
72.475
4.084
71.551
4.036
Social Good
0.862
0.547
0.838
0.516
0.833
0.507
Traffic
0.187
0.304
0.161
0.289
0.164
0.296
Table 18: The ablation studies on modalities.
Models
Aurora
Time-Only
Time + Text
Time Image
Metries
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
Agriculture
0.272
0.348
0.337
0.382
0.304
0.355
0.294
0.351
Climate
0.865
0.749
1.287
0.926
1.167
0.904
1.228
0.897
Economy
0.033
0.146
0.064
0.198
0.046
0.167
0.039
0.152
Energy 0.255
0.370
0.324
0.426
0.292
0.413
0.285
0.406
Eavironment
0.276
0.379
0.352
0.404
0.334
0.397
0.325
0.394
Health 1.553
0.850
2.305
1.147
1.962
0.987
1.874
0.972
Security
72.475
4.084
92.822
5.092
81.294
4.800
77.928
4.628
Social Good
0.838
0.516
1.387
0.692
1.018
0.576
1.037
0.572
Traffic
0.161
0.289
0.345
0.472
0.271
0.418
0.198
0.334
37

---

Published as a conference paper at ICLR 2026
Table 19: Ablation studies on Modality-Guided Attention.
Models
Aurora
Text-Guidance
w/o W
Metrics
MAE
MSB
MAE
MSE
MAE
MSE
MAE
Agriculture
0.272
0.348
0.287
0.350
0.279
0.353
0.274
0.351
Climate
0.865
0.749
0.885
0.767
0.898
0.785
0.876
0.755
Economy
0.033
0.146
0.040
0.157
0.038
0.154
0.034
0.148
Energy 0.255
0.370
0.274
0.387
0.262
0.374
0.265
0.376
Environment
0.276
0.379
0.294
0.386
0.285
0.389
0.280
0.381
Health
1.583
0.850
1.750
0.944
1.688
0.923
1,568
0.859
Security
72.475
4.084
75.742
4.382
76.922
4.482
73.294
4.187
Social Good
0.838
0.516
0.882
0.545
0.868
0.531
0.848
0.522
Traffic
0.161
0.289
0.184
0.296
0.188
0.304
0.166
0.293
Table 20: Zero-shot comparisions among Aurora and other foundation models.
Models
Aurora (zero-shot)
Sundiul (zero-shot)
VisionTS (zero-shot)
ROSE (zero-shot)
MOIRAI (zero-shot)
Metric
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
Agriculture
0.272
0.348
0.373
0.392
0.290
0.336
0,345
0.372
0,272
0.403
Climate
0.865
0.749
1.154
0.881
1.307
0.930
1.475
0.987
1,921
1.095
Economy
0.033
0.146
0.291
0.432
0.301
0.442
0,289
0.433
0.405
0.512
Energy 0.255
0.370
0.272
0.367
0.304
0.420
0.386
0.479
0.324
0.417
Envirumment
0.276
0.379
0.336
0.416
0.354
0.436
0.392
0.456
0.351
0.403
Health 1,553
0.850
1.970
0.992
2.436
1.221
2.598
1.201
2.736
1.241
Security
72.475
4.084
70.441
4.005
79.598
4.597
84.324
4.765
93.245
5.173
Social Good
0.838
0.516
1.036
0.573
1.126
0.618
1.141
0.581
1.430
0.651
Traffic
0.161
0.289
0.271
0.405
0.281
0.407
0.341
0.451
0.406
0.468
Table 21: 10% few shot comparisions among Aurora and other end-to-end multimodal models.
Models
Aurora (10% (ew-shot)
GPTSMMTS (109 few-sbot)
TATS (10% few-shot)
CALF (10% few-shot)
Time TimVVM (10% few-shot)
Metrie
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
Agricalture
0.212
0.293
7.277
1.695
5.793
1.512
0.275
0.344
0.332
0.365
Climate
1015
0.828
1.428
1.477
Beomomy
0.016
0.099
0.274
0.424
0.232
0.390
0.034
0.150
0.273
0.414
Energy
0,230
0.329
0.98
0.730
1.408
0.893
0.473
0.536
0.331
0,433
Enwironment
0.265
0.372
0.738
0.596
0.652
0.564
0.354
0.397
0.437
0.472
Health
1.343
0.776
3.885
1.377
2.781
1.167
1.762
0.939
1.947
0.992
Security
4.670
4.858
181.619
103.113
Social Good
0.494
10.579
1.716
1,500
1.087
1.017
Traffic
0.157
0.290
3.013
1.340
2.613
1.121
0.334
0.122
0.280
0.397
38

---

Published as a conference paper at ICLR 2026
E RELATED WORKS
Time Series Analysis assumes a position of utmost significance within a wide array fields, in- cluding the economy (Qiu al., 2025b;a; Mei al., 2025), transportation (Wu et al., 2025f; 2024; Guo et al., 2014; Liu et al., 2025a), health (Lu et al., 2023; Miao et al., 2024a; Lu et al., 2024; Wang al., 2026a), weather (Li et al., 2025a; Miao al., 2024b; Tian et al., 2026; 2025), and energy (Sun et al., 2025a; Feng et al.). It encompasses numerous crucial tasks, such as forecasting (Li al., 2025b; Cheng al., 2023; Shang al., 2026; 2024), anomaly detection (Wang al., 2025d; Qiu et al., 2025c; Wu et al., 2025e), classification (Liu et al., 2024d), imputation (Wu et al., 2023; 2025b; Wang al., 2025a; Shang Chen, 2024), irregular forecasting (Liu et al., 2026b;a; Yu al., 2025a), motion forecasting (Ma et al., 2025a; 2024a;b; 2025c;b) and others (Qiu et al., 2025f; Wang et al., 2023; Yu et al., 2025b; Cheng et al., 2026; Pan et al., 2023). Among these tasks, Time Series Forecasting is the one most widely applied in real-world situations.
Time series forecasting (TSF) entails the prediction of future observations based on historical ones. Research findings have indicated that features learned through certain methods may exhibit perior performance compared to human-designed features (Liu et al., 2025d; Sun et al., 2025b;a; Niu al., 2025; Chen al., 2023). By harnessing the representation learning capabilities of deep neural networks (DNNs), a multitude of deep-learning based approaches have emerged. For in- stance, TimesNet (Wu al., 2023) and ScgRNN (Lin al., 2023) model time series as vector sequences, employing CNNs or RNNs to capture temporal dependencies. Moreover, Transformer architectures, including Informer (Zhou et al., 2021), Dsformer (Yu et al., 2023), TimeFilter (Hu et al., 2025b), TimeBridge (Liu et al., 2025c), PDF (Dai et al., 2024b), Triformer (Cirstea et al., 2022), PatchTST (Nie et al., 2023), ROSE (Wang et al., 2025f), LightGTS (Wang et al., 2025e), and FLAME (Wu et al., 2025a), are capable of more accurately capturing the complex relationships be- tween time points, thereby significantly enhancing forecasting performance. MLP-based methods, such DUET (Qiu al., 2025c), VAE (Wu al., 2025d), SRSNet (Wu et al., 2025c), AMD (Hu al.,
et al., 2025a), SSF (Lin et al., 2024a), CycleNet (Lin et al., 2024b), NLinear (Zeng et al., 2023), and DLinear (Zeng et al., 2023), adopt relatively simpler architectures with fewer parameters yet still manage to achieve highly competitive forecasting accuracy. Other approaches model the inductive biases within temporal data more effectively from the perspective of loss functions, such as DBLoss (Qiu et al., 2025d), Time-Ol (Wang et al., 2025c), FreDF (Wang et al., 2025b), and DsitDF (Wang et al., 2026b).
39


Reviews:
[{"review_id": "6jlKSnMmW5", "reviewer": "Reviewer_MY6v", "summary": "The authors propose Aurora, a multimodal time series foundation model. By leveraging multimodal information fusion, Aurora achieves impressive zero-shot forecasting performance. The paper also introduces a new decoder design. Experimental results demonstrate that the proposed method outperforms baseline models across various datasets.", "questions": "I am willing to adjust the rating based on the authors' feedback, particularly regarding my concerns about baselines, benchmarks, and experiment settings.\nPlease refer to the weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The authors propose a powerful zero-shot time series foundation model that effectively integrates multimodal information for prediction.\n- The proposed method integrates different modules and achieves remarkable performance in terms of robustness.", "weaknesses": "### **Experiment Setting and Performance**\n\nThe proposed method achieves better zero-shot performance than baselines. However, MAE and MSE are greater than 0.5, as seen in PEMS08, Traffic, and Wind. I am not sure whether the metrics are calculated based on normalized results. If so, a high MAE or MSE may not be better than a straight line or random output.\n\n\n### **Inference time**\n\nThe work focuses on few-shot learning and zero-shot learning. However, compared to the training cost, such a large model increases the inference time and requires more GPU resources. I suggest that the author include an experiment to compare the proposed 16G model with iTransformer on ETT, traffic, or weather, in terms of total running time (training time + inference time for a fair comparison). I am curious about whether the training time and inference time of a small model may be quicker than that of a large model. \n\n### **Baselines**\n\nI noticed the author only chose large foundation models to evaluate few-shot and zero-shot learning. Could the author test small models, e.g., iTransformer and recently advanced methods, on few-shot learning with different budgets, such as 10% and 20%?\n\n### **Full-shot Setting**\n\nDue to the training cost of the small model, the author should also compare their proposed method with unimodal models on full-shot settings, such as PatchTST, iTransformer, TimeXer, or other new advanced methods.\n\n### **Benchmarks**\nAs a time series forecasting task, prediction is 7.5 times the input length, which may not be useful in daily life. Such as forecasting the future one-week weather, but only based on 16 hours. I think the authors should consider making their work more valuable and closer to everyday life settings. Moreover, the author should add more short-term forecasting datasets, such as M4, PEMS, illness, and EPF datasets.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "UVCDGdnBNg", "reviewer": "Reviewer_t5fV", "summary": "This paper presents Aurora, which is a generative foundation model to unify time series forecasting across diverse domains and modalities. First, it has token distillation and modality alignment modules for cross-modality fusion, then the modality-guided attention will inject textual and visual knowledge into temporal encoding. Lastly, in the  decoding stage, a prototype-guided flow matching will improve generative probabilistic forecasting by using learned trend and periodic prototypes. Through the experiments on TimeMMD, TSFM-Bench, and ProbTS, the authors demonstrate Aurora's good performance in zero-shot and few-shot performance across unimodal and multimodal settings.", "questions": "1. Is the use of Flow Matching in the decoder essential, or could other generic multimodal decoding frameworks be applied instead?\n2. Could you provide specific examples where your model demonstrates advantages to your claim: cases where similar historical patterns lead to different future trends due to domain-specific characteristics?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. It really focuses on the limitations of the existing multimodal TS forecaster and foundation models. It introduces a novel multimodal pretraining paradigm with datasets, which is a huge contribution to this field. \n2. The architecture is novel. The modality guided attention bridges domain knowledge with temporal information. The prototype guided flow matching improves the interpretability of probabilistic forecasting.\n3. Aurora is evaluated on diverse benchmarks with both deterministic and probabilistic metrics. The results consistently show the effectiveness of the proposed architectures.", "weaknesses": "1. Using LLM to generate a textual description can be problematic. The paper provides no analysis of data leakage or the quality analysis of generated texts for pretraining data. Also, the paper should provide a more rigorous clarification regarding whether the pretraining data has any overlap with the benchmark datasets.\n2. Prototypes give more interpretability for this paper, but have no analysis or visualization of learned prototypes. \n3. Missing TTM[1] in related work and baseline comparison. \n\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.\" Advances in Neural Information Processing Systems 37 (2024): 74147-74181.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "fEXZOzZFks", "reviewer": "Reviewer_qDZm", "summary": "This work makes a timely and impactful contribution: it is, to the best of my knowledge, the first universal generative foundation model that unifies multimodal inputs (text + image + time series) for cross-domain zero-shot forecasting. Prior foundation models (e.g., MOIRAI, Chronos, Sundial) are unimodal; prior multimodal models (e.g., Time-LLM, CALF, GPT4MTS) are supervised and non-generative. Aurora bridges this gap. The prototype-guided flow matching is also a compelling design that provides interpretable inductive bias (trend/periodicity) to the generative process, improving sample efficiency and forecast realism.", "questions": "See the weaknesses", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- First generative multimodal foundation model enabling cross-domain zero/few-shot time series forecasting.\n- Novel cross-modal encoder (modality-guided attention) and prototype-guided decoder design boost generalization and probabilistic prediction quality.\n- Extensive experiments across diverse benchmarks (TimeMMD, TSFM-Bench, ProbTS) consistently outperform unimodal/multimodal baselines.", "weaknesses": "- Prototype scalability: The prototype bank contains 1,000 learnable patterns. How does performance vary with the number of prototypes (e.g., M = 100 vs. 1,000 vs. 10,000)? Is there a trade-off between forecast accuracy, training stability, and inference latency? A small ablation would clarify whether this component is over-parameterized.\n- The method assumes that text and image modalities are faithful and aligned with the time series. However, in real-world settings: Text may be vague, outdated, or incorrect; and images rely on FFT-based period estimation, which can be unreliable for non-stationary or chaotic series. The paper does not evaluate modality corruption robustness (e.g., masking text, perturbing image tokens), which is critical for practical adoption.\n- Can Aurora be extended to other multimodal time series tasks (e.g., anomaly detection)?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "KDNR5pmwni", "reviewer": "Reviewer_UdWk", "summary": "This paper proposes Aurora, a multimodal time series foundation model for zero-shot forecasting. Aurora is pretrained on cross-domain corpus with time series, text descriptions, and endogenous images. The encoder uses token distillation and modality-guided self-attention to fuse multimodal features. The decoder employs prototype-guided flow matching for probabilistic forecasting, where prototypes encode periodicity/trend initialized from text and image features. Experiments on TimeMMD, TSFM-Bench, and ProbTS show SOTA performance on both multimodal and unimodal tasks.", "questions": "**Q1. Can you provide individual modality ablations?**\n\nReport performance with time-only, time+text, time+image, and time+text+image to quantify each modality's contribution and validate that each adds value.\n\n**Q2. Can you provide finer ablations of modality-guided attention?**\n\nTest text-only guidance, image-only guidance, alternative combination methods, and ablate the learnable metric W to validate design choices in Equations 12-14.\n\n**Q3. Can you visualize and characterize learned prototypes?**\n\nShow visualizations of the 1000 prototypes, analyze whether they form interpretable clusters, and demonstrate which prototypes are retrieved for different domains and patterns.\n\n**Q4. What are typical LLM-generated description examples?**\n\nProvide more examples beyond Figure 7. How do you ensure generated descriptions contain genuine domain knowledge comparable to human expert knowledge? Have you validated description quality through human evaluation or comparison with real domain texts?\n\n**Q5. Can you clarify the zero-shot vs. full-shot comparison?**\n\nIn Table 1, is the zero-shot vs. full-shot comparison primarily demonstrative rather than a rigorous benchmark? Can you add 10%-shot baseline results for fair apples-to-apples comparison?\n\n**Q6. Can you document the full pretraining corpus?**\n\nProvide the complete list of pretraining datasets with names, sizes, and explicit confirmation of which evaluation datasets are excluded to verify no data leakage.\n\n**Q7. Would a unified benchmark be feasible?**\n\nCould you propose or adopt a single unified benchmark covering multimodal, deterministic, and probabilistic forecasting to reduce experimental complexity and improve reproducibility?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**S1. Novel multimodal foundation model architecture**\n\nAurora introduces a well-designed multimodal architecture with token distillation and modality-guided self-attention. The modality-guided mechanism (Equations 12-16) explicitly uses cross-modality correlations to adjust temporal attention, enabling domain knowledge injection. The prototype-guided flow matching is innovative, retrieving learned period/trend prototypes as starting points rather than Gaussian noise. Ablations (Table 4) validate both components with 16-70% MSE increases when removed.\n\n**S2. Strong empirical performance across diverse benchmarks**\n\nAurora achieves SOTA on three benchmarks: rank 1 on MSE/MAE in 31/36 settings on TimeMMD (27% MSE reduction vs. Sundial), 24/28 on TSFM-Bench (15.1% vs. Time-MoE), and 18/23 CRPS/NMAE on ProbTS (21.5% vs. CSDI). The model shows strong zero-shot generalization, outperforming full-shot supervised models on several datasets (Climate, Environment). The 10%-shot results demonstrate data efficiency with 12.8% improvement over GPT4MTS using 90% less training data.\n\n**S3. Comprehensive evaluation coverage**\n\nExperiments span 20 datasets covering multimodal forecasting, unimodal deterministic forecasting, and probabilistic forecasting. The evaluation includes both zero-shot and few-shot scenarios, demonstrating versatility across task types and generalization capabilities.", "weaknesses": "**W1. Insufficient ablation on modality contributions:** Table 4 only ablates entire mechanisms but does not quantify individual modality contributions. The paper lacks experiments comparing time-only, time+text, time+image, and time+text+image performance. This omission makes it impossible to understand what each modality provides. Since images are deterministic transformations of time series (Equations 3-7), readers cannot assess whether images truly add information beyond text or simply provide redundant patterns already in the time series.\n\n**W2. Insufficient ablations on modality-guided attention design:** Variant 1 removes the entire modality-guided mechanism, but the paper lacks finer-grained ablations to validate specific design choices. The Corr matrix computation (Equation 14) multiplies $VAttn$, learnable metric $W$, and $TAttn^T$, but alternative designs (text-only guidance, image-only guidance, additive combination) are not tested. The necessity of the learnable metric W is not validated.\n\n**W3. Confusing experimental setup mixing training regimes:** Table 1 compares 10%-shot Aurora against full-shot baselines, conflating model capability with data efficiency. While this demonstrates Aurora's data efficiency, the comparison is confusing. Zero-shot Aurora outperforms some full-shot models, but readers cannot determine whether advantages come from architecture or simply using less data. A clearer separation of zero-shot comparisons (foundation model vs. foundation model) and few-shot comparisons (Aurora vs. baselines both at 10% data) would eliminate ambiguity.\n\n**W4. No interpretability analysis of learned representations:** The paper provides no visualization or analysis of what Aurora learns. The 1000 learned prototypes in the bank are not visualized or characterized. Whether prototypes cluster into interpretable patterns (linear trends, sinusoids, seasonality) is unknown. Which prototypes are retrieved for different domains is not analyzed. Attention pattern changes with/without modality guidance are not shown. This absence prevents validating that Aurora learns meaningful multimodal representations rather than memorizing dataset-specific patterns.\n\n**W5. Benchmark fragmentation creates unnecessary complexity:** Three separate benchmarks (TimeMMD, TSFM-Bench, ProbTS) use different evaluation protocols, metrics (MSE/MAE vs. CRPS/NMAE), and stride settings. Tables 9-11 in the appendix span many pages with inconsistent experimental configurations. This fragmentation increases complexity and reduces reproducibility. A unified benchmark covering multimodal, deterministic, and probabilistic tasks would streamline evaluation.\n\n**W6. Limited efficiency and scalability analysis:** Table 8 reports 83.5ms inference time, but Figure 5 shows 100 samples are needed for good probabilistic performance. Training time, training cost, and parameter efficiency (accuracy per parameter) are not reported. For a 418M parameter model claiming to be a foundation model, understanding computational requirements and scalability is important for practical deployment.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)