ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting
Authors: Xvyuan Liu
Xiangfei Qiu
Hanyin Cheng
Xingjian Wu
Chenjuan Guo
Bin Yang
Jilin Hu
Venue: ICLR 2026 Poster (2026)
Keywords: Irregular Multivariate Time Series
Time Series Forecasting
Dynamic Graph Neural Networks
Spatio-Temporal Modeling
Data-Driven Interaction
Primary area: learning on time series and dynamical systems

Abstract: Irregular multivariate time series (IMTS) are prevalent in critical domains like healthcare and finance, where accurate forecasting is vital for proactive decision-making. However, the asynchronous sampling and irregular intervals inherent to IMTS pose two core challenges for existing methods: (1) how to accurately represent the raw information of irregular time series without introducing data distortion, and (2) how to effectively capture the complex dynamic dependencies between observation points. To address these challenges, we propose the Adaptive Spatio-Temporal Graph Interaction (ASTGI) framework. Specifically, the framework first employs a Spatio-Temporal Point Representation module to encode each discrete observation as a point within a learnable spatio-temporal embedding space. Second, a Neighborhood-Adaptive Graph Construction module adaptively builds a causal graph for each point in the embedding space via nearest neighbor search. Subsequently, a Spatio-Temporal Dynamic Propagation module iteratively updates information on these adaptive causal graphs by generating messages and computing interaction weights based on the relative spatio-temporal positions between points. Finally, a Query Point-based Prediction module generates the final forecast by aggregating neighborhood information for a new query point and performing regression. Extensive experiments on multiple benchmark datasets demonstrate that ASTGI outperforms various state-of-the-art methods.

Published as a conference paper at ICLR 2026
ASTGI: ADAPTIVE SPATIO-TEMPORAL GRAPH IN- TERACTIONS FOR IRREGULAR MULTIVARIATE TIME
SERIES FORECASTING
Xvyuan Liu'; Xiangfei Qiu'; Hanyin Cheng', Xingjian Wu', Chenjuan Guo', Bin Yang', Jilin Hul.2
Schooo of Data Science and Engineering, East China Normal University, Shanghai, China "Engineering Research Center of Blockchain Data Management, Ministry of Education, China (xvyuanliu, xfqiu, hycheng, x xjwu ecnu. edu.cn jaiguo, byang, jlhu] wwe.daaseeenn..... ecnu. edu. en
ABSTRACT
Irregular multivariate time series (IMTS) are prevalent in critical domains like healthcare and finance, where accurate forecasting is vital for proactive decision- making. However, the asynchronous sampling and irregular intervals inherent to IMTS pose two core challenges for existing methods: (I) how to accurately represent the raw information of irregular time series without introducing data distortion, and (2) how to effectively capture the complex dynamic dependen- cies between observation points. To address these challenges, propose the Graph Interaction (ASTGI) framework. Specifically, the framework first employs Temporall Point Representation module encode each discrete observation as a point within a learnable spatio-temporal embedding space. Second, waighhorhoodAAdaptteo adaptively builds a causal graph for each point in the embedding space via nearest neighbor search. Subsequently, ule iteratively updates information on these adaptive causal graphs by generating messages and computing interaction weights based on the relative spatio-temporal positions between points. Finally, Query Point-based Prediction module gener- ates the final forecast by aggregating neighborhood information for new query point and performing forecasting, Extensive experiments multiple benchmark datasets demonstrate that ASTGI outperforms various state-of- ofttte-arttttt methods.
Adaptive
Graph Construction module
Dynamic Propagation mod-
INTRODUCTION
Irregular Multivariate Time Series Forecasting (IMTSF) is core problem across numerous critical scientific and engineering domains. Its applications are wide-ranging, from monitoring vital signs in intensive care units tracking the evolution of environmental indicators climate science (Yao)
jet al. 2018
Vio et al., 2013 Shukla & Marlin 2020 Brouwer et al. 2019 Bilos et al. 2021 Wang
al.) 2011 Yang et al., This type of data is fundamentally defined by two characteristics: 1) intra-series irregularity, where observations of the same variable occur at unequal time intervals, and 2) inter-series asyn- chrony, where observation timestamps are misaligned across different variables. These traits make challenging to directly apply existing models designed for regular time series. Furthermore, de- spite existing research dedicated to addressing irregular time series prediction tasks (Liu et al.] 2026) Qiu et al. 2025a 2024 Li et al.) 2025b Quu et al.) 2025b), current methodological paradigms are commonly constrained by two core challenges, which impede their application potential in complex real-world scenarios.
al. 2023 Shao et al. 2025: Wu et 2025cl Cheng et al.J [026
2023).
The first challenge how to accurately represent the original information of an irregular time series without introducing information distortion. An accurate representation must preserve the original sampling pattern, as this pattern itself contains key information about the system's 's dynam-
"Equal Contribution

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Figure 1: An illustration of information distortion in mainstream IMTSF paradigms. (a) Raw IMTS. (b) Interpolation-base Converts irregular series into equally spaced series through numerical in- terpolation. Dashed circles denote artificially generated points that were not actually observed. (c) Time-aligned based: Maps the observations of all variables to unified timeline. Points on the zero line denote filled placeholders where no real observation exists for that variable. (d) Patch- aatch-aligned- based: Slices the time series into multiple patches.
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(b) ObE-based Interaction
(c) Static Structure Interaction
(4) ASTGI (Ours)
Figure 2: From fixed interaction rules to adaptive graph interaction. (a) Raw IMTS. (b) ODE-based interaction: Follows the temporal sequence to model continuous dynamics between observations. (c) Static Structure Interaction: Employs a fixed, predefined graph structure, confining information exchange to a static set of connections. (d) ASTGI (Ours): Adaptively constructs a unique graph for each observation point, enabling the capture of complex and dynamic dependencies.
Conversely, any artificial alteration to the orig- inal data structure introduces information distortion, corrupting this intrinsic information and ulti- mately compromising the model's 's predictive performance (Ansari et al. [023] Wang et al.) 2022b cj b0 Qiu al., 2025c). There are three main representation paradigms, each with its own lim- itations. 1) Interpolation -baee Methods (Figure [D:: This paradigm transforms an irregular series into an equally spaced series through numerical interpolation (Shukla & Marlin [02T). However, this approach generates artificial data points that were not actually observed, which can introduce bias and distort the original sampling distribution (Shukla & Marlin 2019: Zhang et al. 2023). 2)
ics (Shukla & Marlin 2020 Li & Marlin 2020).
Time-aligned- based Methods (Figure [+): This paradigm maps the observations of all variables to
unified timeline and fills in the missing values (Brouwer et al., 2019 Wang et al., 2025a Yu et al.] 2025b a). Its main drawback is the loss of precise information about the time intervals between the original observations (Che et al. 2016, Rubanova et al.] 2019). 3) Patch-aligned- based Methods (Figure TT):: This paradigm divides the time series into multiple patches (Cirstea et al., 2022 Nie ct al. 2023] Wu et al.] (2025)). However, rigid division may disrupt the continuity of information. Furthermore, intra-patch aggregation smooth out critical, fine-grained dynamics (Zhang al.,
2024h [uo et al. 2025).
The second challenge lies in how to effectively capture the complex dynamic dependencies among observation points. It is important to note that these two challenges are closely coupled in a progressive manner: accurate representation serves as the prerequisite and foundation for ef- fective dependency modeling. If the raw information is distorted during representation (Challenge 1), the subsequent modeling of dynamic dependencies (Challenge 2) will inevitably be built upon inaccurate data, fundamentally compromising the model's ability to capture true system dynamics. In many real-world scenarios, the interactions among points in an irregular time series are not static; they evolve dynamically over time. Accurately capturing these dependencies is crucial for under- standing the system's 's behavior and making precise predictions (Chen et al.) 2023b Zhang et all, 2022 Qiu et al.) 2025d el Wu et al.) 2025b 2026). However, existing methods generally rely on dre-defined and non-ataptive interaction structures. This means the scope of information exchange between observation points is often limited by set of fixed, prior rules. For instance, in ODE- based Interaction (Figure (2)), information interaction strictly follows the temporal order, meaning an observation can only influence its immediate subsequent state on the timeline and cannot estab-
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lish direct connection with more distant historical observations (Rubanova et al. 2019 Brouwer jet al. 2019; Schirmer et al. 2022 Bilos et al. 2021). Mcanwhile, graph-based methods (Figure 2
construct connections based on fixed rules, such as belonging to the same time point the same
variable Yalavarthi et al., 2024 Li et al. 2025a Chen et al. 2023a Shang & Chen 2024 Shang
et al., 2024; 2026). The common limitation of these methods is their inability dynamically and flexibly identify the truly relevant observation points based on the specific context of each point. As
a result, they struggle to capture the deep dynamic correlations that span across time and variables.
To address the above challenges, we propose the Adaptive Spatio-Temporal Graph Interaction Point Representa-
(ASTGI) framework (Figure This framework begins with a tion module that directly encodes each discrete time series observation into a point within a learnable spatio-temporal embedding space. This method operates on the original set of observation points without requiring interpolation or alignment, thus fully preserving the structure and patterns of the raw data and effectively avoiding information distortion, thereby addressing the first challenge. To cectbboohood-dappiiii Graph Construction module,
tackle the second challenge, we then design a which adaptively builds a causal graph for each point in the embedding space via a nearest-neighbot search, replacing fixed priori interaction rules. Subsequently, the nattio-emmpral Dynamic Propa- gation module performs iterative information updates on these adaptive graphs, generating messages and calculating interaction weights based the relative spatio-temporal positions between points. Finally, the framework's s Query Point-based Prediction module yields the prediction by aggregating information from the neighborhood of a new query point and performing forecasting on
Our main contributions can be summarized as follows:
To address IMTSF, we propose a general framework called ASTGL It learns an accurate forecasting model through adaptive spatio-temporal graph interactions, which effectively avoids information distortion while flexibly capturing dynamic dependencies.
Graph Construction module, which discards pre- defined static interaction structures and adaptively constructs causal interaction graph for each observation point by performing a nearest-neighhor search in the embedding space.
We design the Neighborhood-Adaan
We design a relation-aware dynamic propagation mechanism where message generation and interaction weighting explicitly conditioned the spatio-temporal relative posi- tions between points, enabling the capture of highly context-dependent dynamics.
We conduct extensive experiments on public datasets. The results show that ASTGI out- performs various of baselines. All datasets and code available at https: htt.://nnntteeeleeeee//e///aaa.. ASTGI
2 RELATED WORK
Deep learning has made impressive progress in natural language processing (NLP), time series anal- ysis, computer vision, and other aspects Zhang et al.
(2024a); Yu et al. (2025d e); Zhang et al. (2025a): Chen et al. (2025): y: Lu et al. 2024a 2023): Ma et al. (2024a) b); Zhou et al. (2025), ) Ma al. (2025d); Wang et al. (2026)). Studies have shown that learned features may perform better than human-designed features Zhang et al. (2025c b): Qi et al. (2025); Ma et al. (022cc) Lu et al.
2025): : Ma et al. (2025a bb. Existing deep learning methods for IMTSF can be broadly cate- gorized into two paradigms based how they handle data irregularity: Structured Representation- based methods and Sow-Data-basee methods. The former transforms irregular data into regular structures to utilize standard sequence models, often at the cost of information distortion. The latter models discrete observations directly but typically relies on fixed rules for interaction. Our ASTGI framework falls into the second category but distinguishes itself by employing fully adaptive graph interaction mechanism to capture dynamic dependencies without information loss.
(202bb
2.1
STRUCTURED REPRESENTATION BASSEE METHODS
The core idea of these methods is to convert irregular, asynchronous data into a regular format through structural transformation, thereby making it compatible with standard sequence models. This category includes the following mainstream approaches: (1) Interpolation based Methods: This technique generates new values missing time points through function fitting to create equally
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spaced time series, as adopted in works like mTAN (Shukla & Marlin 2021). ). Its main limitation is the introduction of artificial data points that were not actually observed, which can alter the original data distribution and distort its intrinsic dynamic patterns (Shukla & Marlin 2019; Zhang et al. Methods: This strategy maps the observations of all variables to a unified global timeline and fills in missing values, but this causes loss of precise time interval in- formation between original observations, thereby introducing distortion (Che et al. (0116] Rubanoval
2023). (2) Time-Aligned--aarl
al. 2019 Brouwer et al., 2019). (3) Patch-Aligned- baased Methods: To mitigate the sequence length problem caused by alignment, methods like t- t-PatchGNN (Zhang et al., 2024b) divide the timeline into fixed-size patches for local alignment. However, this aggregation based on prede- fined granularity may smooth out or lose critical fine-grained dynamics within each patch (Luo et al.) 2025 Nie et al. 2023). In contrast, our ASTGI framework directly represents each discrete observa- tion, fully preserving the original data and fundamentally avoiding the information distortion caused by structural transformations.
2.2 RAW-DATA-BASED METHODS
Unlike the previous category, this paradigm models the set of discrete observation points directly, avoiding the distortion introduced by data structuring. However, it typically relies on predefined, non-adaptive rules to capture the dependencies between points. (1) ODE-based Interaction: Rep- resented by models like Latent-ODE (Rubanova et al., 2019) and NeuralFlows (Bilos et al. [021), these methods treat the evolution of a time series as a continuous dynamical system. Although they naturally handle queries any time point, their inherent Markov assumption strictly confines interactions to temporally adjacent states. This prevents the model from capturing direct long- oongrrange dependencies between non-adjacent events (Brouwer et al., 2019 Schirmer et al. (022). (2) Static Structure Interaction: These methods use Graph Neural Networks (GNNs) to learn the relationships between observation points. However, their graph structure is typically constructed based fixed heuristic rules (Yalavarthi et al., 2024] Li et al. 2025a). Such a static topology is insensitive to the specific data context and cannot adapt as the system state evolves, making it difficult cap- ture event-driven dynamic associations. Unlike the fixed interaction rules of these methods, ASTGI adaptively constructs an interaction graph for each observation point, enabling it to dynamically capture context-dependent dependencies.
3 METHODOLOGY
An IMTS sample can be formally represented set of discrete observations S:
(4))
(1)
where S contains N observation tuples. For each tuple (t,, Iy, 2z, t, IR is the timestamp of the observation, x4 E IR is the corresponding observed value, and e E (1,..., .N)] is the variable in- dex, indicating which of the Ne variables the observation belongs to. This set-based representation naturally accommodates irregular sampling intervals and unaligned observations across variables.
For the forecasting task, given split timestamp 4, the sample S is partitioned into historical set Shist and a query set Squery:
Shist = {(1,, F,, G:) E S f S (,), Squery = {(t,, Fy (3) E t, > t.).
(2)
The goal is to learn a forecasting model F. This model takes the historical observation set Shist and set of query coordinates Q [(1,, {,)) as input, and predicts the corresponding set of true values Xy = }. J. The entire forecasting process can be represented as:
F F(Shists
(3)
where Ng is the prediction for the true values X,p
3.1 FRAMEWORK OVERVIEW
Figure shows the adaptive Spatio-Temport Graph Interaction (ASTGI) framework, which com- prises four core stages: (a) Spatio-Temporaa Point Representation, which directly encodes each
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(o) Spatio-Tentt Temporol Point Representation
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Figure 3: Overview of the ASTGI framework. (a) Directly representing each discrete observation maatiotemppoal point. (b) Adaptively constructing causal graph for each point. (c) Iteratively propagating information on the adaptive graphs to update features. (d) Unifying prediction as a neighborhood aggregation task for query point.
discrete observation into a point in spatio-temporal embedding space, preserving the integrity of the original data; (b) Baabbbrhood-AAappiii Graph Construction, which adaptively builds the graph structure and interaction weights based the proximity of points in the embedding space; (c) Opatio-Temporc Dynamic Propagation, which updates point states through a multi-layer graph message passing mechanism capture deep dependencies; and finally, (d) Query Point-based Pre- diction, which unifies the prediction task as a regression problem for a new query point's attributes within this space.
3.2
SETTOOTEMPORALL.. POINT REPRESENTATION
The first step in our framework is to represent each discrete observation (4,, (4, ci) from the histor- ical set Shist as structured spatio-temporal point. This transformation is achieved by introducing three dedicated encoders: (1) a Channel Embedding, which uses learnable embedding matrix Ec E x. to map the variable index O; an embedding vector ec,, capturing the intrinsic rela- tionships between different variables; (2) Time Encoding, which employs learnable Multi-Layer Perceptron (MLP) fP - dRds to map the timestamp I; to a time embedding et,, allowing it to flexibly learn complex temporal patterns; and (3) Value Encoder, which uses another independent MLP x R + gdaata to map the observation value x; into an initial dinot--dimeniionall feature vector h This vector will be iteratively updated in the subsequent dynamic propagation layers.
We concatenate channel embedding and time embedding to form the spatio-temporal coordinate p: for each observation point, which defines its position in learned (d, de -idd-dmiioiolll space:
pj Ce; + Et, E ddo
(4)
where @ denotes vector concatenation. With this, the original set of discrete observations is trans- formed into set of spatio-temporal points {p,, h (0) (;,,),,))) (s) Smis). This representation preserves every original observation point in its entirety, thereby avoiding issues common to inter- polation or alignment paradigms, such information distortion and the introduction of artificial data

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points. The core of this representation lies in the spatio-temporal coordinate space R4++ formed by the channel and time embeddings, which directly serves as a embedding space. It is important to emphasize that the spatial dimension here does not refer to physical geographic location. Instead, an abstract dimension learned from the data, designed to capture the intrinsic relationships between different variables. The subsequent graph construction is then adaptively defined based entirely on the proximity of points within this learned space. Notably, the spatio-temporal coordi- nate p; remains fixed throughout the subsequent propagation process, serving as stable positional anchor, while only the feature vector h; is iteratively refined.
3.3 MEGHIBORRIIOOD-AAAPPE GRAPH CONSTRUCTION
To overcome the limitations of predefined, static interaction structures, we do not presuppose global graph structure. Instead, we adaptively construct directed and weighted causal graph for each spatio-temporal point {(ps, 1 (1, Fi, Q3) E } For clarity, we will refer to an arbitrary spatio-temporal point point and one of its neighboring points as point
3.3.1 CANDIDATE NEIGHBORHOOD IDENTIFICATION
We screen for the most relevant interaction candidates for each point using a two-step process. First, we identify a candidate neighborhood C(t) by searching through all historical points in Spist- Specifically, we select the K points that are closest to point in the learned spatio-temporal coordi- nate space, measured by the Euclidean distance lpp:P; |22. Subsequently. to ensure that information flows only from the past the future, we apply Causal Mask this candidate set, removing all points from C(i) with timestamps later than t to obtain the final, valid set of neighbors (i).
3.3.2 RELATION-AWARY SCORING
The influence of point a point i is quantified by dynamically computed interaction weight asj. Since our information propagation is an iterative process over multiple layers, this weight re-caleulated at each propagation layer l. The core of this calculation is Relation-Aware Scoring function. At the 1-th propagation layer, we define a relation vector r to comprehensively describe the adaptive relationship between two points:
= (p. - p,) @ h @ 1 E (e
(5)
This vector combines the relative position of the two points in the spatio-temporal coordinate space, (p: Pý), with the current features of both interacting parties. This relation vector is fed into
small MLP network, MLP score to generate raw interaction score sty = MLP SeLPscoee(2 (T (!)) Finally, we
apply the Softmax function normalize the interaction scores over all valid neighbors (after causal masking) to obtain the final interaction weight Azy:
exp(si)
aij
(6)
(EN(() exp(si)
3.4 SPATIO-TEMPORAL DYNAMIC PROPAGATION
On the adaptively constructed graphs, we stack information propagation layers to update the point features, thereby capturing long-range and complex spatio-temporal dependencies. At layer the feature h of each point i is updated following a aaassnge-ggregattoonn uppate framework. While the graph topology (ii)) remains fixed across layers, the interaction weights aıy recomputed at each layer based on the updated features, allowing the model to progressively refine its attention neighbors representations evolve.
3.4.1 MESSAGE FUNCTION
The first step in information propagation is define the message passed from neighbor node to node i. To achieve a relation-aware interaction, our message function depends not only on the sender's state h but also explicitly takes the spatio-temporal displacement vector (p: P3) as

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imput. This design allows the model to modulate the transmitted information based the relative
spatio-temporal position of the neighbor to the target point. The message m (!) is generated by Multi-Layer Perceptron network, MLP msgi
J-i
m - MLP msg (h) T 1 (p: - -P))).
(7)
3.4.2 AGGREGATION FUNCTION
Next, node aggregates all incoming messages from its causal neighborhood M(i through weighted sum. The aggregation weights aij are calculated via the dynamic scoring mechanism
in Section 3.3 reflecting the relative importance of each neighbor in the current interaction. This aggregation operation can adaptively focus on the most informative neighbors:
(1) mi
Σ JEN((
aij m m
(8)
3.4.3 UPDATE FUNCTION
Finally, we use an update module with residual connection and Layer Normalization to update point i's feature, integrating its own historical information with the aggregated neighborhood infor- mation:
h
(1+1) LayerNorm (h!) MLP MLPPapdate
update (m )))
After L layers of propagation, we obtain the feature representation h (L)
for each observation point.
3.5 QUERY POINT-BASED PREDICTION
We unify the prediction task into this framework. A prediction request for target time ty and target variable cg is treated as a query point, and its value is predicted by applying regression to the ag- gregated information from its historical neighborhood. The key difference between this prediction process and the feature propagation stage lies in the network modules used. Instead of reusing the scoring and message networks from the feature propagation layers, we design separate set of scor- ing and fusion networks specifically for the forecasting task. This parameter separation allows the model optimize independently for two functionally distinct sub-tasks iterative feature updating during multi- layer propagation and direct numerical regression at the final prediction step-therebb improving prediction flexibility and accuracy.
3.5.1 QUERY POINT EMBEDDING AND NEIGHBORHOOO IDENTIFICATION
We use the same encoders as for the historical points to map the query coordinates (ty, Cy) spatio-temporal position p, = Ec, + Lr(t)). Subsequently, we retrieve its K nearest neighbors from all historical spatio-temporal points to form its neighborhood Mgg).. The causality is naturally satisfied as all historical points precede the query point in time.
3.5.2 QUERY RELATION SCORING AND WEIGHTED FUSION
We use a query relation scoring network MLP querqueryscoone- dedicated prediction, compute the association score Sqi between the query point P, and each of its neighbors i E (()).. This score depends on the spatio-temporal relative position between the two points and the neighbor's final feature state (L) after L layers of propagation:
(.)). (n)
Syi = MLP query score
(10)
These scores sni are normalized through Softmax function obtain of interaction weights agi. Finally, these weights are used to perform a weighted fusion of neighbor information to generate fusion vector hy Before aggregation, we also use value network MLP transform the neighbor features to extract the most valuable information segments for the prediction:
hg
Ogi MLP value (h (h(
(11)
(EN(g)
Finally, this fusion vector h.g, which aggregates spatio-tempore information from the neighborhood, fed into final Regression Head Phead output the predicted value S. P (h.
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Table 1: Statistical overview of the four benchmark datasets used our experiments.
Property
MIMIC
PhysioNet
Human Activity
USHCN
Domain
Healthcare
Healthcare
Biomechanics
Climate
# Samples
21,250
11,981
1,359
1,114
# Variables
96
36
12
5
Max Sequence Length
96
47
131
337
Avg # Observations
144.6
308.6
362.2
313.5
3.6 TRAINING ORJECTIVE
The entire ASTGI model is end-to-end differentiable. During training, the model takes the history set Sais as input to predict the value zj for each query coordinate (t). from the query set Syuery- We jointly optimize all model parameters by minimizing the Mean Squared Error (MSE) loss function L over all queries:
C
Σ
(ă, 2z)²
(12)
Qquery
(1
where ē is the prediction for the query (fy. and 2. is its ground truth value.
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
We conduct experiments on four widely-used public IMTS datasets span- ning diverse application domains: MIMIC, PhysioNet, Human Activity, and USHCN. A statistical overview of these datasets is provided in Table These four benchmarks collectively cover broad spectrum real-world irregularity characteristics, ranging from high- nigh-dimensional clinical records (MIMIC with 96 variables) to oiw-dimeniional climate monitoring (USHCN with 5 variables), and from densely sampled motion signals (Human Activity) to sparsely observed physiological indi- cators (MIMIC). To ensure faimess and comparability of the results, all datasets preprocessed by strictly following the standard procedures established in prior state-of-the-art works (Yalavarthi al., [2024] Zhang et al. 2024b). We uniformly split the data into training, validation, and test sets with ratio of 80%. 10%, and 10%, respectively. We selected a total of twelve state-of-the-art models from two main categories designed for irregular time series as baselines for comparison. For a detailed description of the datasets and baselines, please refer to the Appendix B.] and B.2
Datasets and Baselines.
Implementation Details. All our experiments were conducted on server equipped with an NVIDIA A800 GPU and implemented using the PyToorch 2.6.0+cu 1 124 framework. All models are trained using the Mean Squared Error (MSE) as the loss function and optimized with the AdamW optimizer. We set the maximum number of training epochs to 300 and employ early stopping strategy, where training is terminated the model's performance on the validation set does not improve for 5 consecutive epochs. To ensure fair comparison across all models, primarily adopted the hyperparameter settings reported in the original papers for the baseline models. Build- ing on these configurations, the validation set for some models to ensure that each achieved a competitive level of performance. To ensure reproducibility and mitigate the effects of randomness, each experiment is run indepen- dently with five different random seeds, and we report the mean and standard deviation. Detailed hyperparameter configurations for all models are provided the Appendix B.2
conducted further search and fine-tuning of key hyperparameters
4.2 MAIN RESULTS
We present the performance comparison of ASTGI against the selected baselines on four public datasets see Table 2 We have the following key observations: (1) ASTGI achieves state-of-the-an prediction accuracy across all datasets. Compared to the second-best performing model, Hi-Patch, ASTGI achieves significant reductions of approximately 6.04% MSE. (2) ASTGI demonstrates consistent and superior performance across diverse domains. On datasets from healthcare (MIMIC,
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Table 2: Forecasting performance on four IMTS datasets. Overall performance evaluated by MSE and MAE (mean + std). The best and second-best results are highlighted in bold and with an underline, respectively.
Dataset
Human Activity
USHCNN
PhysioNet
MIMIC
Metric
MSE
MAE
MSE
MAE
MSE
MAE
MSE
MAE
PrimoNet
0041
1,7088000011
0.4930+0.0015
0:4054x0.0018
0,7053=0
06889H000000
0.907300.0001
0.6614:0.000)
NourdlRooss
0.1722a0 0090
0.319010.000 0094
0.208760.0258
0.3157H0.0187
0.00
0.446610.0027
0. 00608500.0101
0.5306e0.000 0066
CRU
0.13
0.26076000092
0.2168:001622
0,3180=0.0248
0,6179H0.004S 0044
0.5778:0.0031
0. 0.589580.0092
0.5151
IIAN
0,09956000266
0:221960:0047
0.0501+0.2020
0.5015 000
0.3807=00003
04029100.0035
0.940840
0.6755e00.0499
SeFT
1.576G000024
0.976200.0077
0.3345+0.0022
0,40
0.7721=000021
0.676H0000029
0.925009.0015
0.602
GNeuralFlow
0.391620.158
0.0511
0.220510.042)
0.3286:0.0412
0:8207.0.0010
0.679910:1000
0.8957s0.02099
0.6450.0.0072
GRU-D
0.189020.0662
0.32531000488
0,209720.0493
0.301510.0005
0.3419:0.0029
0.299240.0011
0.475910.0100
0.426600.0055
Raindrup
0.091620.0022
0.21141000072
0.203540.0336
0,302920.0266
0,3478:0.0019
0.401110:0020
0.675:1a0.1829
0.5642000066
Warpformer
0.0442000000
0.122840.0018
0.1 0.1898+0.0595
0.299900 0191
10500000001
0.3611+0.0016
0.430240.0035
0.4025H0.0144
iPanAACNNN
0.443-00009
0.124740:0081
0.1885000440
0.506400.0479
0,5133=0:003
0:3977000049
0:445100.0115
0.407H00.008
GraFITi
0,0132200005
0:122120.0011
+0,093
0,2777.0.0248
0.30752000055
0.363740.0036
0:435920.0445
0.414220.097
Hi- Patch
0.0
0.120640.0009
0.176920.0268
0.271700/016
0.30710000029
0.367520.0042
0.427930.0010
0.0000000002
ASTGI (Ours)
0,641220.005
0.1181:9.0010
0.160810.0100
0.2597.0.0155
0.309420.0006
0.358920.0015
0.390920.0017
0.365220.000
3.00
64-
Mart
MSE
3.20
322
030
666
embedding d_c
d_model
(a) K
Lb)
embedding d.c
(d) d_model
Figure 4: Parameter sensitivity studies of main hyper-parameters in ASTGI.
PhysioNet), biomechanics (Human Activity), and climate science (USHCN), ASTGI consistently outperforms all competing methods, highlighting its strong generalization capability and robustness.
The superior performance of ASTGI can be attributed to its innovative modeling paradigm, which effectively addresses the two core challenges outlined in the introduction. First, by directly repre- senting discrete observations as Temmooal Points, ASTGI completely avoids data alignment or interpolation, thus preserving the integrity of the original information. Second, and more crit- ically, it replaces interaction structures that rely on fixed rules with a data-driven Neighborhood- Adaptive Graph Construction mechanism. This allows the model to adaptively identify the most relevant neighbors for each observation point and to capture complex dependencies across time and variables through the subsequent Opatio-Temports Dynamic Propagation process.
4.3 PARAMETER SENSITIVITY
To investigate the ASTGI framework's dependency on key hyperparameters, we performed a sensi- tivity analysis on the number of candidate neighbors (K), number of propagation layers (L), model hidden dimension (dmode)). and channel embedding dimension (d.)-see see Figure 4 The analysis re- veals that the model's performance not overly sensitive to these parameters and remains stable within reasonable range. Specifically: (1) K: Performance stabilizes once K reaches thresh- old sufficient to capture key information. This indicates that our Neighborhood- Srighborhood-Adapiie Graph Construction mechanism can effectively identify and utilize the most important neighbors without overly relying on a precise or large neighborhood size. (2) L: Generally, a small number of layers is sufficient to capture complex spatio-temporal dependencies. Too many layers can introduce the risk of ever-smoothing on complex datasets, leading to a slight decrease in performance. (3) dmwxtel and de: The optimal choice is directly related to the intrinsic complexity of the dataset. The model

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Table 3: Ablation study of ASTGI components. Results are reported in MSE (mean + std). The performance of our full model is highlighted in bold.
Model/ Dataset
Human Activity
USHCN
PhysioNet
MIMIC
w/o Leamed Courdinates
0.0421 0.0421=0.0002
0.1838:10.00
0.3034:10.00 0020
0.4057:10.001
w/o Adaptive Graph
0.1830=:000233
0.3164:0.0000
0.4065=0.0004
w/o Relation-Aware
0.041810.0000
0.1930.10.0244
0.3072.10.0030
0.419410.0034
rp. Mean Pooling
0.087011.0056
0.169910000105
0,482611000150
0.8807110.0036
ASTGI (ours)
0.0412
0.1607:000010
0.3004:1
0.3909:0.0017
Table 4: Ablation study of ASTGI components. Results are reported in MAE (mean + std). The performance of our full model is highlighted in bold.
Model/ Dataset
Human Activity
USHCN
PhysioNet
MIMIC
w/o Learned Courdinates
0.276910.0153
0.3620.10.002
0.3932.10.0044
w/o Adaptive Graph
0.1 0.40000007
0,2892+0.0117
0.8712++0.0034
0.3948+00.006
w/o Relation-Aware
0.1210+0.0038
0.3002+:0.0211
0.0064
0.4023++0.0023
rp. Mcan Pooling
0.1973=0.0083
0,2684+:001466
0.5028+000110
0.6492:#0.006,
ASTGI (ours)
0.1181+
0,2597+ 0.0155
0,3589 0.0015
0.3852++0.004
requires sufficient representational power to encode data patterns, but overly high dimensions can increase the risk of overfitting, especially for datasets with fewer variables.
4.4 ABLATION STUDIES
To verify the effectiveness of each component in the ASTGI framework, we conduct series of ablation studies-see Table R for the MSE metric and Table 4 for the MAE metric. We draw the following four key conclusions. (1) the learnable coordinate space enhances representation power. Replacing the learnable time and channel embeddings with fixed, non- non- paraaereetticc encodings leads to significant drop in performance. This indicates that adaptively learned metric space is crucial for capturing the unique non-linear patterns and inter-variable correlations within the data, (2) the data-driven adaptive graph is superior to static structure. Degrading the basis for neighborhood search from the learned spatio-temporal coordinates to the original timestamps results in a notable performance decline. This demonstrates that adaptively discovering neighbors in the learned metric space is more effective than relying on fixed rules of temporal proximity. (3) the relation-aware propagation mechanism improves interaction precision. Removing the spatio-temporal displace- ment vector (p: -P;) as input when calculating interaction weights and messages causes signif- icant performance degradation. This highlights that modulating information based on the relative spatio-temporal position of neighbors is vital for capturing relation-dependent dynamics. (4) the dedicated query aggregation mechanism outperforms simple pooling. Replacing the interaction- weighted fusion in the prediction stage with simple neighborhood average pooling leads to a sub- stantial drop in performance. This confirms that differentially weighting neighbor information based on its spatio temporal relationship to the query point allows the model to focus more effectively critical information during prediction.
5 CONCLUSION
This paper introduces the ASTGI framework to address the core challenges of information distortion and static interactions in IMTSF, At its core, the framework maps each discrete observation directly into a learnable spatio-temporal embedding space. This design fundamentally avoids the informa- tion distortion caused by data preprocessing steps like interpolation or alignment. More critically, ASTGI abandons predefined interaction structures. Instead, it constructs a data-driven causal neigh- borhood graph for each point within the embedding space and employs relation- aware propagation mechanism to precisely model complex dynamics that span across time and variables. Experimental results across multiple public datasets consistently show that ASTGI achieves significantly higher prediction accuracy than state-of-the-art methods, demonstrating that this adaptive graph interaction paradigm an effective and promising new direction for solving irregular time series problems.
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ACKNOWLEDGEMENTS
This work was partially supported by the National Natural Science Foundation of China (No. 62372179, No. 62472174) and the Fundamental Research Funds for the Central Universities. Jilin Hu is the corresponding author of the work.
ETHICS STATEMENT
Our work exclusively uses publicly available benchmark datasets that contain personally identi- fiable information. No human subjects are involved in this research.
REPRODUCIBILITY STATEMENT
The promise that all experimental results can be reproduced. We have released our model code in an anonymous http:pppp/gggg....oo./////aaaaaa
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A THE USE OF LARGE LANGUAGE MODELS (LLMS)
We do not use Large Language Models in our methodology and writing.
B DETAILED EXPERIMENTAL SETUP
B.1 DATASETS DETAILS
In this section, we provide a detailed description of the four public datasets used in our experiments, including their sources, characteristics, and the specific preprocessing steps applied.
MIMIC large, freely-accessible critical care database (Johnson et al. 2016). contains de- identified health data from patients who stayed in intensive care units (ICUs) the Beth Israel Deaconess Medical Center between 2001 and 2012. The dataset is highly detailed, including vital signs, medications, and lab measurements. For our experiments, we use the clinical time series data from the first 48 hours of each patient's ' ICU stay. The MIMIC dataset contains 21,250 samples with 96 variables.
PhysioNet dataset is another valuable resource for clinical time series analysis (Johnson et al., 2014). was released for a challenge to predict the in-hospital mortality of ICU patients. The dataset includes records from 12,000 ICU stays, with each record consisting of multivariate time series of measurements from the first 48 hours. comprises 11,981 samples and 36 variables, such as serum glucose and heart rate.
Human Activity dataset from the UCI Machine Learning Repository used for research human activity recognition (Rubanova et al., 2019). It contains data from sensors placed on the ankles, belt, and chest of five individuals performing various activities. The dataset includes 1,359 samples and 12 variables representing 3D positions. The data is naturally irregular because the sensors record information at slightly different time intervals.
USHCN (United States Historical Climatology Network) dataset offers long-term climate data from weather stations across the United States, covering over 150 years (Menne et al. 2015). It a key resource for studying climate change. The dataset includes 1,1 14 samples and 5 variables, such as daily maximum and minimum temperatures and precipitation. Although data is recorded daily, missing observations are common, which makes it suitable for irregular time series analysis. In our study, we use a subset of the data from a 4-year period between 1996 and 2000, following the approach of previous work.
B.2 BASELINE MODEL DETAILS
IMTS CLASSIFIAATION/IPUUAAAN MODELS
For all baseline models originally designed for classification, replace the final Softmax layer with a linear layer to adapt them for the forecasting task.
PRIMENET (CHRO ET AL. 2023
PrimeNet is pre-training model for IMTS. Our experiments load its official pre-trained weights and fine-tune it on each dataset. The model's patch length varies by dataset, the number of heads is to and the learning rate x 10
SEFT HHRRN ET AL. 2020
SeFT processes all observation points in a time series as unordered set. The model consists of 2 layers, dropout rate of 0, and a learning rate x 10
MTAN (SHUKLA & MARLIN 2021
mTAN utilizes multi-time attention mechanism map features from irregular series onto fixed set reference points. The number of reference points is set to 32 for the MIMIC dataset and defaults to for others. The learning rate is 1 10-3
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Published as a conference paper at ICLR 2026
GRU-D (CHE ET AL. 2016
GRU-D an adaptation of the Gated Recurrent Unit (GRU) for IMTS with missing values. In our experiments, learning rate of 1 x 10-3 is used for this model.
RAINDROP ZIIANG ET AL. 2022)
Raindrop graph attention model for IMTS. Its hidden dimension is 32. The learning rate is 1 x 10-3 for the HumanActivity dataset and 10-4 for others. The number of heads is 4 for the Humannctivityy and USHCN datasets and defaults to a different value for others.
WARPFORMER ZHANG ET AL. 2023)
Warpformer uses warping technique for multi-scale modeling. It has hidden dimension of 256, attention heads, dropout rate of 0, and layers. The learning rate x 10-3
IMTS FORECASTING MODELS
NEURALFFLOWS BBLOO ET AL., 2021
NeuralFlows is model based on ordinary differential equations (ODEs). includes flow layers, a latent dimension of 20, time encoding hidden dimension of 8, and uses 3 hidden layers. The 10 10-3
learning rate
CRU (CHIRMER ET AL.. 2022
The CRU model uses continuous recurrent units to handle irregular time series. Its hidden dimension set to 20, and the learning rate is 1 10-3
GNEURALFLOW (MERCATALI ET AL.
2024
GNeuralFlow enhances NeuralFlows by incorporating graph neural networks, It uses ResNet as its flow model with flow layers. The input latent dimension 20, the time encoding hidden dimension is 8, and it has hidden layers. The learning rate is 10-3.
TPATCHGNN (ZHAN ET AL. 2024B
IPatchGNN first processes an IMTS into patches and then uses graph neural network for forecast- ing. The patch length varies depending on the dataset. The number of heads is set to 1, and the learning rate x 10-3
GRAFITI (AALAVARTHI ET AL. 2024
GraFITi uses bipartite graphs to represent irregular time series. Its latent dimension is 256 for the MIMIC dataset and 128 for others. The number of layers is set for MIMIC and USHCN, and for the remaining datasets. The learning rate is 1 x 10-3
HI-PATCH (LUO AL.. 2025
Hi-Patch is pasch-based hierarchical Transformer model. Its hidden dimension, number of heads, and patch length are specifically set for each dataset. The learning rate is 5 10-1 for the PhysioNet dataset and 10-3 for others.
C QUALITATIVE ANALYSIS OF LEARNED INTERACTION GRAPHS
To validate the interpretability and effectiveness of the proposed NeighborhooddAAaqphee Graph Construction module, we visualize the learned causal graph for randomly selected sample from the MIMIC test set.
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Published as a conference paper at ICLR 2026
PH
02 Saturation
Hemoglobin
6CS total
Lactate
Calcium ionized
Bands
Bilirubin
Creatinine
Temperature
Bilirubin direct
Troponin
1
Time (Hours)
Visualization of the adaptively learned causal graph. The plot displays the interac- tions between observation points for a sample from the MIMIC dataset. The x-axis represents time (hours), and the y-axis represents different variables. Arrows indicate the direction of information flow (from history to query). The model successfully captures (1) synchronous correlations between variables (e.g.. Bilirubin Direct and pH at 1 8), (2) long-range temporal dependencies (e.g.. Tro- ponin I self-connection t = 4 + t = 7), and (3) cross-variable lagged effects. This confirms that ASTGI adaptively constructs sparse and meaningful interaction topology.
Figure 5:
Figure 5 illustrates the inference process. In this graph: (1) Nodes represent discrete observa- tions, positioned horizontally by timestamp (Time) and vertically by variable type. (2) Node Size is proportional to the cumulative attention weight the node receives, indicating its importance in the current inference context. (3) Edges represent the learned attention scores. Darker and thicker lines indicate stronger dependencies. To ensure visual clarity, only visualize the top-K strongest incoming edges for each node.
As shown in Figure the learned structure exhibits three distinet patterns that align with the char- acteristics of irregular multivariate time series: (1) Synchronous Multivariate Correlations: The model frequently establishes strong connections between different variables observed the same timestamp. For example, at t 8, significant interaction is observed between Bilirubin Direct and pH (indicated by the thick connecting line). This demonstrates that ASTGI can effectively lever- age synchronous co-occurrence information to reconstruct the system state, bypassing the need for manual time alignment. (2) Direct Long- Range Dependencies: Unlike recurrent models that prop- agate information step-by-step, ASTGI enables direct information propagation across non-adjacent timestamps. A clear example is the variable Troponin where the observation at 4 strongly connects to the subsequent observation at 7. This mechanism allows the model to retrieve crit- ical historical information directly, thereby mitigating the long-term dependency issues common in recurrent architectures. (3) Variable Temporal Impact: The graph also captures complex lagged dependencies across different variables. We observe that the state Troponin / at t = exerts notable influence Hemoolobii l 8. This suggests that the model has learned asso- ciate early anomalies in specific physiological indicators with delayed responses in others, capturing the systemic dynamics of the underlying process.
D VISUALIZATION OF LEARNED SPATIO-TEMPOR EMBEDDINGS
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Published as a conference paper at ICLR 2026
To intuitively understand how ASTGI represents discrete irregular observations, provide a vi- sualization analysis of the learned spatio-temporal coordinate space. We randomly selected a test sample from the MIMIC dataset and extracted the coordinate vectors p; ec, et, for all its observation points. We then utilized t-SNE to project these high-dimensional coordinates into a 21) space.
Figure illustrates the resulting embedding structure. To clearly visualize the temporal evo- lution within the embedding space, we apply time-dependent color gradient the obser- vation points of each variable. Specifically, lighter shades represent earlier observations, while darker and more saturated shades indicate later timestamps. The visualization highlights three key characteristics: (1) Variable Distine- tiveness: Observations corresponding to dif- ferent variables (represented by different col- ors) form distinct, well-separated clusters. This demonstrates that the learnable Channel Em- bedding effectively captures the unique seman- tic identities of different physiological indica- tors. (2) Temporal Continuity: For any given variable, the observation points do not collapse into single spot but instead form continu- ous trajectory. The smooth transition from light to dark colors confirms that our Time Encod- ing successfully preserves the sequential order and temporal intervals within the embedding space. (3) Validity for Adaptive Graph Con- struction: The combination of variable clus- tering and temporal trajectories creates a struc- tured metric space. In this space, the Euclidean distance-used by our Neighborhood-AAoe Adaptiie Graph Construction modulc-naturally prioritizes neighbors that semantically correlated and temporally relevant. This validates our design choice of replacing fixed interaction graphs with dynamic k-NN search in this leamned space.
Verisbic
648
BCS
Ceroum
Weight
Trepersture
Latent Dimension
Latent Dimension
Figure 6: Visualization of the learned spatio- temporal embedding space. We visualize the co- ordinates of observations from MIMIC sam- ple using t-SNE. Points are colored by variable types. The temporal evolution is indicated by the color intensity: for each variable, the color transitions from light (early time) to dark (late time). The clear clustering and continuous gradi- ents demonstrate that ASTGI effectively encodes both variable semantics and dynamic temporal patterns.
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Reviews:
[{"review_id": "fmw7RoneTB", "reviewer": "Reviewer_2C1m", "summary": "This paper proposes ASTGI, a framework that models irregular multivariate time series by representing each observation as a spatio-temporal point and dynamically constructing adaptive neighborhood graphs for message passing and forecasting. \nThe core idea is to treat each timepoint as a node and learn relations based on temporal and channel embeddings, followed by adaptive graph propagation and query-based prediction. Experimental results on several IMTS benchmarks demonstrate improvements over prior methods.", "questions": "Could the authors report training and inference wall-clock time and GPU memory comparison between ASTGI and key baselines (e.g., CRU, Warpformer, tPatchGNN, HyperIMTS if added)? Nearest-neighbor search among N nodes generally incurs at least $O(NK)$, and naive top-K selection may approach $O(N^2)$ if not optimized. For typical IMTS settings with thousands of points per sample, this can become computationally heavy.", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The overall framework is clearly described and modularized, including point representation, adaptive neighbor discovery, and dynamic propagation.\n- The authors conduct thorough ablation studies to justify several architectural design choices.", "weaknesses": "- A central concern is that the conceptual contribution is significantly overlapped with HyperIMTS (Li et al., ICML 2025). HyperIMTS also models each observation timepoint as a node and constructs a relational structure on top of it using temporal and channel information, enabling information aggregation over learned irregular-TS graph topology. Although the exact mechanisms differ (hypergraph vs nearest-neighbor graph), the core idea (treating each observation as a graph node and learning adaptive relational structure in irregular time series) is shared. The authors cite HyperIMTS in Introduction, but do not compare its performance in Experiment, which is problematic for a fair positioning of contributions.\n- The paper motivates adaptive graph construction as a key novelty and advantage. However, the experimental section only evaluates forecasting performance and ablations, without providing any qualitative or quantitative analysis of the learned graph structures.", "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": "s3CPAf20Ek", "reviewer": "Reviewer_LeMp", "summary": "The paper proposes ASTGI, a novel framework for Irregular Multivariate Time Series Forecasting (IMTSF), which addresses two core challenges: (1) avoiding information distortion caused by interpolation or alignment, and (2) capturing complex dynamic dependencies between observations. ASTGI introduces a learnable spatio-temporal embedding space where each observation is represented as a point. It then adaptively constructs causal interaction graphs via nearest-neighbor search and performs relation-aware message propagation to model dependencies. A query-based prediction module is used to forecast future values. Extensive experiments on four public datasets show that ASTGI outperforms state-of-the-art baselines, and ablation studies confirm the importance of each component.", "questions": "1. What is the relationship between raw information representation and dynamic dependency modeling in IMTS?\n2. Regarding Candidate Neighborhood Identification:\n(1) Why is distance measured in the coordinate space rather than the feature space?\n(2) Why is Euclidean distance used instead of Cosine similarity?\n(3) Why is normalization not applied? Does the embedding norm influence performance?\n(4) Why are points with distant coordinates discarded? Could this remove long-range dependencies?\n(5) How is the top-k operation differentiated? Might it suppress useful neighbors during early training stages?\n3. What is the novelty of the Spatiotemporal Dynamic Propagation module compared to existing message-passing mechanisms in Graph Neural Networks?\n4. Both the raw interaction score in Equation (3) and the message in Equation (4) incorporate the relative positional embedding $p_i - p_j$. How do their roles differ?\n5. In Line 322, why does such a separation improve flexibility and accuracy?\n6. Can the authors provide visualized analyses of the spatiotemporal points in the embedding space?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. IMTSF is an interesting topic. The idea of representing IMTS as points in a learnable spatio-temporal space and adaptively constructing causal graphs effectively bridges the gap between raw data representation and dynamic dependency modeling.\n2. The methodology is clearly described, and the authors have provided code and detailed experimental setups, which supports reproducibility.\n3. The paper includes experiments on four diverse datasets (healthcare, climate, activity) and compares against 12 strong baselines. The results are convincing and statistically sound.", "weaknesses": "1. The connections between the two key challenges are not clearly explained.\n2. In the Candidate Neighborhood Identification module, the choice of Euclidean distance in the spatiotemporal coordinate space lacks sufficient justification.\n3. The technical contributions of the Neighborhood-adaptive Graph Construction and Spatiotemporal Dynamic Propagation modules are limited.\n4. The Related Work section lacks an overarching overview.\n5. There is a typo in Equation (8): $a_{q,i}$ should be $s_{q,i}$.", "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": "OZgWlm8o3m", "reviewer": "Reviewer_FrHL", "summary": "This paper addresses two fundamental challenges in Irregular Multivariate Time Series Forecasting (IMTSF): 1) information distortion caused by data preprocessing like interpolation, and 2) the inability of static interaction structures to capture complex, dynamic dependencies. To tackle these issues, the paper proposes a novel framework named ASTGI. The framework preserves raw information by directly embedding each discrete observation into a learnable spatio-temporal space. It then adaptively constructs a causal graph for each point to capture dynamic relationships. Finally, it performs prediction through relation-aware message passing on these graphs. Experimental results on four public datasets show that ASTGI significantly outperforms a wide range of state-of-the-art baselines.", "questions": "1.\tRegarding the model's computational efficiency, what is the theoretical computational complexity of constructing the adaptive graph for each observation point, and are there practical strategies to optimize this process?\n2.\tRegarding the hyperparameter K, what was the design consideration for using the same value for both the information propagation and the final prediction stages?\n3.\tIf the K-nearest neighbors in the learned space are all in the future, the causal mask results in zero neighbors. How does the model handle this \"neighbor starvation\"?\n4.\tWhy use a separate scoring network for the final prediction, instead of reusing the one from the propagation phase? What's the advantage of this design?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1.\tIt solves the information distortion problem in irregular time series modeling by directly mapping discrete observations to a learnable continuous space, avoiding any preprocessing.\n2.\tIts data-driven, adaptive graph construction mechanism discards all prior assumptions about interactions, enabling the model to capture highly complex and dynamic dependencies across time and variables.\n3.\tIt seamlessly unifies the prediction task into its neighborhood aggregation framework, showing a high degree of conceptual consistency and design elegance that many other methods lack.\n4.\tIt consistently outperforms a large number of strong baselines across multiple diverse benchmarks, providing powerful evidence of the framework's superior performance and broad applicability.", "weaknesses": "1.\tAn analysis of the model's computational complexity and its scalability on larger-scale datasets is not provided, making it difficult to assess its deployment potential in resource-constrained environments.\n2.\tThe motivation for using a unified hyperparameter K across different modules is not sufficiently clarified; setting them separately might offer further optimization for neighborhood aggregation at different stages.\n3.\tAfter multi-layer graph propagation, the initial observation $x_i$'s magnitude information could be diluted, affecting final regression accuracy. The paper lacks discussion on mitigating this risk.", "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)