Pith. sign in

REVIEW 3 major objections 4 minor 2 cited by

HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Irregular time series forecasting can skip padding by treating every observation as a hypergraph node and passing messages along time and variable hyperedges.

desk verdict A serious empirical benchmark with a clean non-padding model, but the paper's central claim that hypergraph incidence drives the results is not supported by the equations: the incidence matrices are defined and never used again. read the letter →

arxiv 2505.17431 v1 pith:HDXAJUOH submitted 2025-05-23 cs.LG

Boyuan Li , Yicheng Luo , Zhen Liu , Junhao Zheng , Jianming Lv , Qianli Ma This is my paper · ORCID
classification cs.LG
keywords irregularmultivariatetimeserieshypergraphneuralnetworkforecastingmessagepassingirregularity-awaresimilaritymissingvaluesnodepredictionefficientrepresentation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that irregular multivariate time series, where each variable is sampled at its own unaligned times, can be forecast accurately without padding the data into a regular grid. Its model, HyperIMTS, turns each observed value into a node in a hypergraph and links nodes with temporal hyperedges and variable hyperedges, so that messages flow among all observations through three stages of message passing. On five standard benchmarks it reports the lowest mean squared error, with up to 11.4% improvement over the best irregular-series competitor, while processing only the observed values rather than padded matrices. A reader should care because, if the claim holds, hypergraphs offer a single representation that captures both temporal and cross-variable dependencies directly from raw irregular samples.

What carries the argument

The load-bearing object is the irregularity-aware similarity blend that drives inter-variable message passing. For each pair of variables, a time-aware similarity $S_{obs}$ is computed from dot products of observation nodes that share timestamps, and an overall similarity $S_{var}$ from the variable hyperedge embeddings; the two are combined as $\mathrm{S}_{IMTS} = \alpha S_{obs} + (1-\alpha)S_{var}$ with $\alpha = T_{shared}/T_{total}$ when $S_{var} > \delta$ and $S_{obs} \neq 0$, and $\alpha = 0$ otherwise, with $\delta$ a learnable threshold initialized to 0.5. This lets the model use fine-grained aligned comparisons when variables are largely aligned and fall back to whole-series comparisons when they are not, a choice that is only possible because the hypergraph gives observation nodes a unified place where temporal and variable hyperedge messages can meet.

What would settle it

Retune the strongest baselines per dataset, especially GraFITi and tPatchGNN, with a full hyperparameter search and end-to-end training of the classification-derived baselines on the same five benchmarks; if HyperIMTS no longer holds the lowest MSE on at least two datasets, the reported up-to-11.4% margin rests on the particular baseline configuration rather than the model.

Watch

Extended reading notes

Core claim

HyperIMTS converts an irregular multivariate time series into a hypergraph where each observation is a node, temporal hyperedges connect observations sharing a timestamp, and variable hyperedges connect observations of the same variable. Forecasting becomes node prediction: the nodes to be forecast are zero-initialized and updated by three message-passing stages—node-to-hyperedge attention that refreshes temporal and variable hyperedge embeddings, hyperedge-to-hyperedge attention that passes messages between variables, and hyperedge-to-node updates that propagate both kinds of information back to observations. The distinctive mechanism is the irregularity-aware variable similarity, which blends a time-aware similarity computed only on time-aligned observation pairs with an overall series-level similarity between variable hyperedges, choosing the mix by the fraction of shared timestamps with a learnable threshold. The paper reports the lowest MSE on all five datasets in Table 1 (the text counts four, attributing the exception to USHCN's high variance) and up to 11.4% improvement over GraFITi.

Load-bearing premise

The superiority over the twenty-seven baselines assumes those baselines are run close to their best settings under the paper's policy of reusing original hyperparameters and adapting classification models by replacing the final softmax layer with a linear layer.

Editorial extensions

If this is right

  • Padding becomes unnecessary: HyperIMTS operates only on observed values, avoiding the data-volume growth that canonical and patch-aligned padding introduce.
  • Both temporal and variable dependencies are learned inside one hypergraph, so irregular forecasting reduces to a node prediction problem on that hypergraph.
  • The irregularity-aware similarity lets the model adaptively weight time-aligned versus whole-series comparisons, addressing partial alignment between variables.
  • On the five benchmarks the model reports the lowest MSE, up to 11.4% better than the next-best irregular-series model GraFITi, with lower computational cost than padding-based alternatives.
  • Because non-padding models keep efficiency roughly constant as lookback length grows, the approach scales better to long irregular windows than padding-based models.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The time-aware/overall similarity blend could be lifted out of the hypergraph and inserted into set-based or bipartite-graph forecasters, potentially giving those models a cheap way to handle partially aligned variables.
  • The node-prediction framing may transfer to imputation and classification tasks, since any masked node is already treated as a prediction target by zero-initialization.
  • The efficiency argument depends on observation count being much smaller than the padded grid; on nearly dense irregular series the attention costs would rise quadratically and the practical gap over padding methods would shrink.
  • A synthetic dataset with controlled alignment ratios (from fully shared to fully disjoint timestamps) could isolate exactly when the time-aware term pays off and when the fallback to overall similarity is what preserves performance.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper proposes HyperIMTS, a hypergraph neural network for forecasting irregular multivariate time series without padding. Observations are represented as nodes, and temporal and variable hyperedges connect them; the model alternates node-to-hyperedge attention, irregularity-aware hyperedge-to-hyperedge attention among variables, and hyperedge-to-node updates, followed by a linear decoder. The method is evaluated on five IMTS datasets in a unified benchmark against 27 baselines, reporting the lowest MSE on four datasets and up to 11.4% improvement over GraFITi, with additional ablations and efficiency analyses.

Significance. The paper's strengths are the breadth of the benchmark (27 baselines, five datasets, five seeds), the public code release, and the careful ablation isolating temporal/variable hyperedges and irregularity-aware dependencies. If the architecture is corrected to enforce the claimed hypergraph incidence structure, the hypergraph formulation is an appealing way to avoid padding and to model cross-variable dependencies without shared timestamps. The significance is conditional on the structural issue discussed below, since the equations as written do not enforce the claimed hypergraph topology.

major comments (3)
  1. [§4.1–§4.2, Eqs. (2), (6), (12), (14), (15)] The incidence matrices H_T and H_U defined in Eq. (2) are never used after their definition. Eq. (6) computes the updated temporal hyperedge with unmasked softmax attention over all M observation nodes, so a temporal hyperedge at time t receives messages from observations at every other timestamp; the analogous variable-hyperedge update is also unmasked over all nodes. Eq. (12) builds a complete attention graph among all U variable hyperedges, and Eqs. (14)–(15) concatenate the full hyperedge embeddings to every node rather than gathering only the hyperedges incident to that node. As written, the model is therefore a global set-attention network with timestamp and variable tokens, and the central claim that irregularity-aware hypergraph topology drives the empirical gains is not supported. Please either reintroduce masks/gathers based on H_T and H_U in Eqs. (6), (12), (14), and (15), or explicitly present and defend the global-attention interpretation.
  2. [§A.4 (baseline details)] The statement 'For all classification models, we replace the final softmax layer with a linear layer to enable forecasting' is a substantial architectural modification for SeFT, mTAN, Raindrop, and Warpformer, whose original training objectives and hyperparameters are classification-oriented. No validation is provided that this linear-head adaptation is a strong or fair forecasting baseline, and the paper's conclusion of superiority over 27 state-of-the-art models depends on this protocol. Please justify the adaptation, report any tuning performed for the forecasting head, and consider comparing against published forecasting results or a tuned forecasting version of the strongest classification baselines.
  3. [§4.2.2, Eqs. (10)–(11)] The text states that α 'prioritize[s] Sobs over Svar if there are more aligned observations than unaligned ones,' but Eq. (11) sets α = Tshared/Ttotal for any Svar > δ and Sobs != 0, without requiring Tshared/Ttotal > 0.5. For a pair with Tshared/Ttotal = 0.2, α = 0.2 and Svar still dominates, contradicting the stated logic. Either add the condition Tshared/Ttotal > 0.5 to Eq. (11) or revise the explanation to reflect the actual weighting.
minor comments (4)
  1. [§4.1] The hypergraph definition 'E := ET ∩ EU' should presumably be a union, not an intersection, since temporal and variable hyperedges are disjoint sets; as written the notation is inconsistent with the subsequent text.
  2. [§5.2] 'MIMIC-VI' is a typo for MIMIC-IV; the text also calls GraFITi the 'overall next best model,' which is true on average across the four datasets where HyperIMTS leads, but not on USHCN, where Warpformer and GRU-D have lower MSE than GraFITi.
  3. [§A.4.2 and Appendix A.3] Appendix A.4.2 contains the typo 'numeber' for 'number'; Appendix A.3 contains the typos 'data volumn' for 'data volume' and 'grouth' for 'growth'.
  4. [§4.2.1] The phrase 'making them learnable' is imprecise: Eq. (4) fixes sinusoidal encoding and applies a learnable linear map FF_time, so the temporal hyperedge embeddings are learnable only through that map.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the evaluation is external and no fitted quantity is relabeled as a prediction; the incidence-matrix inconsistency is a consistency/correctness concern, not a circular derivation.

full rationale

HyperIMTS is an empirical forecasting architecture evaluated on held-out test splits against twenty-seven external baselines. The learnable quantities (node/hyperedge embeddings, projection layers, and the threshold δ initialized to 0.5) are trained by MSE on the lookback/forecast split and are not derived from test outcomes. No equation constructs a prediction from a quantity that was itself fit to that prediction, so the 'fitted input called prediction' pattern does not occur. The comparisons to GraFITi and other baselines are measured on independent test data, and no uniqueness theorem or first-principles claim depends on a self-citation. The self-citation to Luo et al. (2024) appears in related work and is not load-bearing for the method's validity. The one substantive concern raised by the architecture is that Eq. (2) defines incidence matrices H_T and H_U, but the update equations (6), (12), and (14) use unmasked attention over all observation nodes and concatenate hyperedge embeddings without referencing H_T or H_U. If the implementation follows the equations literally, the claimed hypergraph locality is not enforced. This is a consistency/correctness issue that could undercut the interpretation of the empirical gains, but it is not circularity: no result is assumed as its own input, and no fitted value is renamed as a prediction.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central claim rests on four assumptions: that a hypergraph with temporal and variable hyperedges can express the needed dependencies, that sinusoidal time embeddings retain irregular sampling information, that the learned gating rule (Eq. 10-11) is a sufficient inductive bias, and that the 27 baselines are fairly tuned. Most are standard domain assumptions; the gating rule and the baseline fairness are the least supported by independent evidence.

free parameters (1)
  • Threshold delta = learned (initialized 0.5)
    Eq. (11) uses delta to decide when to blend time-aware similarity S_obs into the inter-variable attention; the value is learned during training and its sensitivity is not analyzed.
assumptions (4)
  • domain assumption Hypergraph message passing over timestamp- and variable-defined hyperedges captures the temporal and cross-variable dependencies needed for IMTS forecasting.
    Section 4.2 presumes that three message-passing phases (node-to-hyperedge, hyperedge-to-hyperedge, hyperedge-to-node) are sufficient to model dependencies without explicit recurrent or ODE dynamics.
  • domain assumption Sinusoidal encoding of timestamps (Eq. 4) preserves enough temporal information for irregularly sampled data.
    Section 4.1 uses sin(FF_time(T_i)) as the temporal embedding; this assumes the continuous time values at integer indices capture the irregularity.
  • ad hoc to paper The gating rule in Eqs. (10)-(11) with a learned threshold delta is an effective inductive bias for varying degrees of alignment.
    This rule is introduced specifically for HyperIMTS; no external evidence or theoretical analysis supports the particular form or the delta threshold.
  • domain assumption Baselines in the unified benchmark are tuned adequately and adapted fairly for the forecasting task.
    Section 5.1.2 and Appendix A.4 assert that original hyperparameters are reused and classification models are converted to forecasting by swapping the final layer; a failure here would inflate claimed gains.

how reviews work

0 comments
Cite this review

Pith. "Pith review of HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting." pith.science (2026). https://pith.science/paper/HDXAJUOH

@misc{pith2026250517431,
  author       = {Pith},
  title        = {Pith review of: HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HDXAJUOH}},
  note         = {Machine review of arXiv:2505.17431}
}
read the original abstract

Irregular multivariate time series (IMTS) are characterized by irregular time intervals within variables and unaligned observations across variables, posing challenges in learning temporal and variable dependencies. Many existing IMTS models either require padded samples to learn separately from temporal and variable dimensions, or represent original samples via bipartite graphs or sets. However, the former approaches often need to handle extra padding values affecting efficiency and disrupting original sampling patterns, while the latter ones have limitations in capturing dependencies among unaligned observations. To represent and learn both dependencies from original observations in a unified form, we propose HyperIMTS, a Hypergraph neural network for Irregular Multivariate Time Series forecasting. Observed values are converted as nodes in the hypergraph, interconnected by temporal and variable hyperedges to enable message passing among all observations. Through irregularity-aware message passing, HyperIMTS captures variable dependencies in a time-adaptive way to achieve accurate forecasting. Experiments demonstrate HyperIMTS's competitive performance among state-of-the-art models in IMTS forecasting with low computational cost.

Figures

Figures reproduced from arXiv: 2505.17431 by the authors.

Figure 1
Figure 1. Existing methods for processing IMTS sample. (a) Canonical padding approach, which significantly increases the amount of data. (b) Patch-aligned padding approach, which also increase the amount of data. (c) Set views all observations as its unordered items. (d) Bipartite graph uses observation edges to connect variable and time nodes. It cannot model dependencies between variables without aligned observations, like … view at source ↗
Figure 2
Figure 2. Illustration of the proposed efficient hypergraph repre￾sentation for IMTS. (a) Original IMTS sample, where V1, V2, and V3 represent three different variables. (b) The corresponding hy￾pergraph representation. From top to bottom, variable hyperedges, observation nodes, and temporal hyperedges are displayed. Each observation node connects to the associated variable hyperedge above and the temporal hyperedge below. Gr… view at source ↗
Figure 3
Figure 3. The architecture of HyperIMTS. It first converts input IMTS samples with empty forecast targets into the proposed efficient hypergraph representation. Three types of message passing are used sequentially: (a) Temporal and variable hyperedge embeddings are updated via node-to-hyperedge message passing; (b) Inter-variable correlations are modeled during irregularity-aware hyperedge-to￾hyperedge message passing, where … view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Model efficiency comparison on MIMIC-III, with 36 hours of lookback length, 3 forecast timestamps, 96 variables, and a batch size of 32. Our proposed model, HyperIMTS, achieves the lowest MSE while maintaining high computational efficiency, as measured by training time…
Figure 5
Figure 5. Figure 5: Forecasting performance with varying lookback lengths and fixed forecast horizons. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: Efficiency comparison with varying lookback lengths and fixed forecast horizons on MIMIC-III. The efficiency of non-padding methods is relatively insensitive to the increase in lookback length. A.3. Additional Efficiency Analysis We further analyze the efficiency of no…
Figure 7
Figure 7. Figure 7: Efficiency comparisons on MIMIC-IV, PhysioNet’12, Human Activity, and USHCN. tPatchGNN and Warpformer run out of GPU memory on MIMIC-IV and are not plotted. Efficiency for non-padding methods is similar to other approaches on shorter lookback length like PhysioNet’12 a…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Cost-Effective Multimodal LLM Reasoning Framework for Question Answering over Irregular Clinical Time Series

    cs.AI 2026-07 conditional novelty 6.0 of 10

    ClinPRISM reaches 49.83% average accuracy on CLIR-Bench irregular clinical time-series QA using a 4B LLM, 16 temporal tokens, and 0.15 s/question.

  2. Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework

    cs.LG 2026-07 conditional novelty 5.0 of 10

    WrapFlow combines continuous-time event/gap tokenization with simulation-free residual flow matching on a Transformer to improve irregular multivariate time-series forecasting.

Reference graph

Works this paper leans on

50 extracted references · 34 canonical work pages · cited by 2 Pith papers

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

  2. [2]

    S., Januschowski, T., and Günnemann, S

    Biloš, M., Sommer, J., Rangapuram, S. S., Januschowski, T., and Günnemann, S. Neural Flows : Efficient Alternative to Neural ODEs . In Advances in Neural Information Processing Systems , volume 34, pp.\ 21325--21337. Curran Associates, Inc. , 2021

    work page 2021

  3. [3]

    Recurrent Neural Networks for Multivariate Time Series with Missing Values

    Che, Z., Purushotham, S., Cho, K., Sontag, D., and Liu, Y. Recurrent Neural Networks for Multivariate Time Series with Missing Values . Scientific Reports, 8 0 (1): 0 6085, 2018. ISSN 2045-2322. doi:10.1038/s41598-018-24271-9

    doi:10.1038/s41598-018-24271-9 2018

  4. [4]

    O., Yoder, N

    Chen, S.-A., Li, C.-L., Arik, S. O., Yoder, N. C., and Pfister, T. TSMixer : An All-MLP Architecture for Time Series Forecast-ing . Transactions on Machine Learning Research, 2023. ISSN 2835-8856

    work page 2023

  5. [5]

    R., Li, J., Zhang, X., Hong, D., Gupta, R

    Chowdhury, R. R., Li, J., Zhang, X., Hong, D., Gupta, R. K., and Shang, J. PrimeNet : Pre-training for Irregular Multivariate Time Series . Proceedings of the AAAI Conference on Artificial Intelligence, 37 0 (6): 0 7184--7192, 2023. ISSN 2374-3468. doi:10.1609/aaai.v37i6.25876

    work page doi:10.1609/aaai.v37i6.25876 2023

  6. [6]

    Filling the G \_ap\_s: Multivariate Time Series Imputation by Graph Neural Networks

    Cini, A., Marisca, I., and Alippi, C. Filling the G \_ap\_s: Multivariate Time Series Imputation by Graph Neural Networks . In International Conference on Learning Representations , 2021

    work page 2021

  7. [7]

    Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting

    Cini, A., Mandic, D., and Alippi, C. Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting . In International Conference on Machine Learning , pp.\ 8985--8999. PMLR , 2024

    work page 2024

  8. [8]

    GRU-ODE-Bayes : Continuous Modeling of Sporadically-Observed Time Series

    De Brouwer, E., Simm, J., Arany, A., and Moreau, Y. GRU-ODE-Bayes : Continuous Modeling of Sporadically-Observed Time Series . In Advances in Neural Information Processing Systems , volume 32. Curran Associates, Inc. , 2019

    work page 2019

Show all 50 references
  1. [9]

    HGNN +: General Hypergraph Neural Networks

    Gao, Y., Feng, Y., Ji, S., and Ji, R. HGNN +: General Hypergraph Neural Networks . IEEE Transactions on Pattern Analysis and Machine Intelligence, 45 0 (3): 0 3181--3199, 2023. ISSN 1939-3539. doi:10.1109/TPAMI.2022.3182052

    2023

  2. [10]

    and Dao, T

    Gu, A. and Dao, T. Mamba: Linear-Time Sequence Modeling with Selective State Spaces . In First Conference on Language Modeling , 2024

    2024

  3. [11]

    BigST : Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road Networks

    Han, J., Zhang, W., Liu, H., Tao, T., Tan, N., and Xiong, H. BigST : Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road Networks . Proc. VLDB Endow., 17 0 (5): 0 1081--1090, 2024. ISSN 2150-8097. doi:10.14778/3641204.3641217

    2024

  4. [12]

    Set Functions for Time Series

    Horn, M., Moor, M., Bock, C., Rieck, B., and Borgwardt, K. Set Functions for Time Series . In International Conference on Machine Learning , pp.\ 4353--4363. PMLR , 2020

    2020

  5. [13]

    Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-w. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., and Mark, R. G. MIMIC-III , a freely accessible critical care database. Scientific Data, 3 0 (1): 0 160035, 2016. ISSN 2052-4463. doi:10.1038/sdat...

    2016 doi

  6. [14]

    Johnson, A. E. W., Bulgarelli, L., Shen, L., Gayles, A., Shammout, A., Horng, S., Pollard, T. J., Hao, S., Moody, B., Gow, B., Lehman, L.-w. H., Celi, L. A., and Mark, R. G. MIMIC-IV , a freely accessible electronic health record dataset. Scientific Data, 10 0 (1): 0 1, 2023. ...

    2023 doi

  7. [15]

    Reformer: The Efficient Transformer

    Kitaev, N., Kaiser, L., and Levskaya, A. Reformer: The Efficient Transformer . In International Conference on Learning Representations , 2019

    2019

  8. [16]

    Spatial- Temporal Hypergraph Self-Supervised Learning for Crime Prediction

    Li, Z., Huang, C., Xia, L., Xu, Y., and Pei, J. Spatial- Temporal Hypergraph Self-Supervised Learning for Crime Prediction . In 2022 IEEE 38th International Conference on Data Engineering ( ICDE ) , pp.\ 2984--2996. IEEE Computer Society , 2022. ISBN 978-1-66540-883-7. doi:10....

    2022

  9. [17]

    iTransformer : Inverted Transformers Are Effective for Time Series Forecasting

    Liu, Y., Hu, T., Zhang, H., Wu, H., Wang, S., Ma, L., and Long, M. iTransformer : Inverted Transformers Are Effective for Time Series Forecasting . In The Twelfth International Conference on Learning Representations , 2023

    2023

  10. [18]

    Directed hypergraph attention network for traffic forecasting

    Luo, X., Peng, J., and Liang, J. Directed hypergraph attention network for traffic forecasting. IET Intelligent Transport Systems, 16 0 (1): 0 85--98, 2022. doi:10.1049/itr2.12130

    2022 doi

  11. [19]

    Knowledge- Empowered Dynamic Graph Network for Irregularly Sampled Medical Time Series

    Luo, Y., Liu, Z., Wang, L., Wu, B., Zheng, J., and Ma, Q. Knowledge- Empowered Dynamic Graph Network for Irregularly Sampled Medical Time Series . In The Thirty-eighth Annual Conference on Neural Information Processing Systems , 2024

    2024

  12. [20]

    Long-term daily and monthly climate records from stations across the contiguous united states (u.s

    Menne, M., Williams, Jr., C., and Vose, R. Long-term daily and monthly climate records from stations across the contiguous united states (u.s. historical climatology network). 1 2016. doi:10.3334/CDIAC/CLI.NDP019

    2016 doi

  13. [21]

    Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series

    Mercatali, G., Freitas, A., and Chen, J. Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series . In The Thirty-eighth Annual Conference on Neural Information Processing Systems , 2024

    2024

  14. [22]

    H., Sinthong, P., and Kalagnanam, J

    Nie, Y., Nguyen, N. H., Sinthong, P., and Kalagnanam, J. A Time Series is Worth 64 Words : Long-term Forecasting with Transformers . In The Eleventh International Conference on Learning Representations , 2022

    2022

  15. [23]

    Rubanova, Y., Chen, R. T. Q., and Duvenaud, D. K. Latent Ordinary Differential Equations for Irregularly-Sampled Time Series . In Advances in Neural Information Processing Systems , volume 32. Curran Associates, Inc. , 2019

    2019

  16. [24]

    Sawhney, R., Agarwal, S., Wadhwa, A., Derr, T., and Shah, R. R. Stock Selection via Spatiotemporal Hypergraph Attention Network : A Learning to Rank Approach . Proceedings of the AAAI Conference on Artificial Intelligence, 35 0 (1): 0 497--504, 2021. ISSN 2374-3468. doi:10.160...

    2021 doi

  17. [25]

    Modeling Irregular Time Series with Continuous Recurrent Units

    Schirmer, M., Eltayeb, M., Lessmann, S., and Rudolph, M. Modeling Irregular Time Series with Continuous Recurrent Units . In International Conference on Machine Learning , pp.\ 19388--19405. PMLR , 2022

    2022

  18. [26]

    Ada- MSHyper : Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting

    Shang, Z., Chen, L., Wu, B., and Cui, D. Ada- MSHyper : Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting . In The Thirty-eighth Annual Conference on Neural Information Processing Systems , 2024

    2024

  19. [27]

    Shukla, S. N. and Marlin, B. Interpolation- Prediction Networks for Irregularly Sampled Time Series . In International Conference on Learning Representations , 2018

    2018

  20. [28]

    Shukla, S. N. and Marlin, B. Multi- Time Attention Networks for Irregularly Sampled Time Series . In International Conference on Learning Representations , 2020

    2020

  21. [29]

    Shukla, S. N. and Marlin, B. M. A Survey on Principles , Models and Methods for Learning from Irregularly Sampled Time Series . 2021. doi:10.48550/arXiv.2012.00168

    2021 doi arXiv

  22. [30]

    J., Celi, L

    Silva, I., Moody, G., Scott, D. J., Celi, L. A., and Mark, R. G. Predicting In-Hospital Mortality of ICU Patients : The PhysioNet / Computing in Cardiology Challenge 2012. Computing in cardiology, 39: 0 245--248, 2012

    2012

  23. [31]

    CSDI : Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation

    Tashiro, Y., Song, J., Song, Y., and Ermon, S. CSDI : Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation . In Advances in Neural Information Processing Systems , volume 34, pp.\ 24804--24816. Curran Associates, Inc. , 2021

    2021

  24. [32]

    DiffSTG : Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models , 2023

    Wen, H., Lin, Y., Xia, Y., Wan, H., Zimmermann, R., and Liang, Y. DiffSTG : Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models , 2023

    2023

  25. [33]

    Unified Training of Universal Time Series Forecasting Transformers

    Woo, G., Liu, C., Kumar, A., Xiong, C., Savarese, S., and Sahoo, D. Unified Training of Universal Time Series Forecasting Transformers . In International Conference on Machine Learning , pp.\ 53140--53164. PMLR , 2024

    2024

  26. [34]

    Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

    Wu, H., Xu, J., Wang, J., and Long, M. Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting . In Advances in Neural Information Processing Systems , volume 34, pp.\ 22419--22430. Curran Associates, Inc. , 2021

    2021

  27. [35]

    TimesNet : Temporal 2D-Variation Modeling for General Time Series Analysis

    Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., and Long, M. TimesNet : Temporal 2D-Variation Modeling for General Time Series Analysis . In The Eleventh International Conference on Learning Representations , 2022

    2022

  28. [36]

    Connecting the Dots : Multivariate Time Series Forecasting with Graph Neural Networks

    Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., and Zhang, C. Connecting the Dots : Multivariate Time Series Forecasting with Graph Neural Networks . In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , KDD '20, pp.\ 753--763. ...

    2020

  29. [37]

    K., Madhusudhanan, K., Scholz, R., Ahmed, N., Burchert, J., Jawed, S., Born, S., and Schmidt-Thieme, L

    Yalavarthi, V. K., Madhusudhanan, K., Scholz, R., Ahmed, N., Burchert, J., Jawed, S., Born, S., and Schmidt-Thieme, L. GraFITi : Graphs for Forecasting Irregularly Sampled Time Series . Proceedings of the AAAI Conference on Artificial Intelligence, 38 0 (15): 0 16255--16263, 2...

    2024 doi

  30. [38]

    Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification

    Yan, Y., Qin, J., Chen, J., Liu, L., Zhu, F., Tai, Y., and Shao, L. Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification . In Proceedings of the IEEE / CVF Conference on Computer Vision and Pattern Recognition , pp.\ 2899--2908, 2020

    2020

  31. [39]

    FourierGNN : Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective

    Yi, K., Zhang, Q., Fan, W., He, H., Hu, L., Wang, P., An, N., Cao, L., and Niu, Z. FourierGNN : Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective . In Thirty-Seventh Conference on Neural Information Processing Systems , 2023

    2023

  32. [40]

    J., and Leskovec, J

    You, J., Ma, X., Ding, Y., Kochenderfer, M. J., and Leskovec, J. Handling Missing Data with Graph Representation Learning . In Advances in Neural Information Processing Systems , volume 33, pp.\ 19075--19087. Curran Associates, Inc. , 2020

    2020

  33. [41]

    I., Qin, J., and Wang, S

    Yu, G., Zou, J., Hu, X., Aviles-Rivero, A. I., Qin, J., and Wang, S. Revitalizing Multivariate Time Series Forecasting : Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling . In Forty-First International Conference on Machine Learning , 2024

    2024

  34. [42]

    Adaptive Hypergraph Learning and its Application in Image Classification

    Yu, J., Tao, D., and Wang, M. Adaptive Hypergraph Learning and its Application in Image Classification . IEEE Transactions on Image Processing, 21 0 (7): 0 3262--3272, 2012. ISSN 1941-0042. doi:10.1109/TIP.2012.2190083

    2012

  35. [43]

    Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series

    Zhang, J., Zheng, S., Cao, W., Bian, J., and Li, J. Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series . In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pp.\ 3273--3285, 2023 a . doi:10.1145/3580305.3599543

    2023

  36. [44]

    Self- Supervised Learning for Time Series Analysis : Taxonomy , Progress , and Prospects

    Zhang, K., Wen, Q., Zhang, C., Cai, R., Jin, M., Liu, Y., Zhang, J., Liang, Y., Pang, G., Song, D., and Pan, S. Self- Supervised Learning for Time Series Analysis : Taxonomy , Progress , and Prospects . 2023 b . doi:10.48550/arXiv.2306.10125

    2023 doi arXiv

  37. [45]

    Irregular Multivariate Time Series Forecasting : A Transformable Patching Graph Neural Networks Approach

    Zhang, W., Yin, C., Liu, H., Zhou, X., and Xiong, H. Irregular Multivariate Time Series Forecasting : A Transformable Patching Graph Neural Networks Approach . In Forty-First International Conference on Machine Learning , 2024

    2024

  38. [46]

    Graph- Guided Network for Irregularly Sampled Multivariate Time Series

    Zhang, X., Zeman, M., Tsiligkaridis, T., and Zitnik, M. Graph- Guided Network for Irregularly Sampled Multivariate Time Series . In International Conference on Learning Representations , 2021

    2021

  39. [47]

    and Yan, J

    Zhang, Y. and Yan, J. Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting . In The Eleventh International Conference on Learning Representations , 2022

    2022

  40. [48]

    Learning with Hypergraphs : Clustering , Classification , and Embedding

    Zhou, D., Huang, J., and Schölkopf, B. Learning with Hypergraphs : Clustering , Classification , and Embedding . In Advances in Neural Information Processing Systems , volume 19. MIT Press , 2006

    2006

  41. [49]

    Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

    Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting . Proceedings of the AAAI Conference on Artificial Intelligence, 35 0 (12): 0 11106--11115, 2021. ISSN 2374-3468. doi:10....

    2021 doi

  42. [50]

    FEDformer : Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

    Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., and Jin, R. FEDformer : Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting . In International Conference on Machine Learning , pp.\ 27268--27286. PMLR , 2022

    2022

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.