ALER-TI: Aligned Latent Embedding Retrieval for Time Series Imputation

Nhat-Hai Nguyen; Thi-Thu Nguyen; Trung-Kien Le; Tung Kieu; Xuan-Thong Truong

REVIEW 3 major objections 8 minor 29 references

ALER-TI: Aligned Latent Embedding Retrieval for Time Series Imputation

T0 review · 3 major / 8 minor · reviewed 2026-07-09 · glm-5.2

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Pith's one-line read Retrieval-augmented imputation matches on-the-fly masking at a fraction of the cost

desk verdict Retrieval-augmented imputation with a real efficiency idea, but the core mechanism is under-formalized and under-ablated read the letter →

arxiv 2607.07640 v1 pith:PRVOPX3V submitted 2026-07-08 cs.LG cs.AI

Xuan-Thong Truong , Trung-Kien Le , Tung Kieu , Thi-Thu Nguyen , Nhat-Hai Nguyen This is my paper

classification cs.LGcs.AI

keywords aler-tiimputationlatentseriestimeembeddinghistoricalretrieval

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 proposes ALER-TI, a retrieval-augmented framework for time series imputation that retrieves relevant historical subsequences to supplement corrupted local context. The central technical contribution is Latent Embedding Alignment (LEA), which addresses a fundamental tension in retrieval-based imputation: the query is corrupted by missing values while historical candidates are complete, creating a representation mismatch that degrades retrieval quality. Two existing strategies represent the extremes of the trade-off space. Asymmetric Retrieval encodes queries and candidates independently, allowing offline caching but suffering from representation mismatch. On-the-fly Masking applies the query's missingness pattern to each candidate before encoding, eliminating the mismatch but requiring online re-encoding of the entire database for every query, which is computationally prohibitive. LEA resolves this tension by shifting the interaction with the missingness pattern from the raw input encoding stage to a downstream latent alignment stage. Candidates are encoded once in a mask-agnostic manner and cached offline. At inference time, LEA uses a candidate-guided query encoding mechanism — a small set of learnable query codes that summarize the observed portions of the corrupted query, then aggregate using attention weighted by each candidate's cached representation — to compute similarity scores that account for the missingness pattern without re-encoding candidates. The paper demonstrates that this approach achieves imputation quality comparable to or better than on-the-fly masking while preserving the efficiency of cached retrieval, and that it consistently improves ten different backbone architectures across six datasets and four missing rates.

What carries the argument

Latent Embedding Alignment (LEA): a dual-stream retrieval mechanism with (1) mask-agnostic offline candidate encoding via a Transformer encoder, (2) candidate-guided query encoding using m learnable query codes that attend to observed tokens and then re-attend to candidate representations, and (3) post-hoc latent masking that applies the query's missingness pattern in the latent space. Training uses an InfoNCE contrastive objective with STL-decomposed positives (trend + seasonal components) and Pearson-correlation-based hard negative mining. Retrieved candidates are fused with frozen backbone outputs via a gated adapter with RevIN normalization.

What would settle it

Construct a time series with a deliberate regime change at the train/test boundary — where the test period follows a fundamentally different generative process than anything in the training split — and show that ALER-TI's gains over the standalone backbone vanish or reverse, while the adapter-only variant (random retrieval or no retrieval) retains any observed improvement, demonstrating that the gains come from the adapter rather than meaningful retrieval.

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Extended reading notes

Core claim

The core discovery is that post-hoc masking in latent space — applying the query's missingness pattern after, rather than before, candidate encoding — can align corrupted queries with clean historical candidates nearly as well as on-the-fly masking, while avoiding the need to re-encode the candidate database for each query. This is made effective by a candidate-guided query encoding mechanism in which a small number of learnable codes summarize the observed query context and then dynamically re-weight their attention based on each candidate being compared against, producing a query representation that is both mask-aware and candidate-specific without touching the candidate encoder at runtime

Load-bearing premise

The candidate pool built from the training split contains structurally relevant subsequences for test-time queries. If the test period exhibits distributional shift such that no historically similar patterns exist in the training data, the retrieval mechanism provides no benefit or may introduce noise.

Editorial extensions

If this is right

Time series imputation backbones can be treated as frozen feature extractors and augmented with external historical memory, separating the parametric modeling capacity from the non-parametric retrieval capability — this suggests a design pattern where simpler, cheaper backbones paired with good retrieval may match the performance of larger, more complex architectures.
The post-hoc latent masking principle may generalize beyond imputation to any retrieval setting where the query is corrupted or incomplete — for example, retrieval-augmented forecasting under sensor degradation, or cross-modal retrieval where query and candidate modalities produce systematically different representations.
The finding that gains are largest for simple linear backbones (DLinear, RLinear) and still substantial for strong architectures (TimesNet, ModernTCN) suggests that explicit non-parametric memory and parametric model capacity are partially substitutable, which has implications for model design in resource-constrained settings.
The contrastive training with STL-decomposed positives encouraging alignment to deterministic structural patterns rather than stochastic noise implies that retrieval quality depends on separating signal from noise in both the query and the candidate, which could inform retrieval mechanism design in other noisy-data domains.

Reading between the lines

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

If the candidate pool is drawn from the training split and the test period exhibits distributional shift with no structurally similar historical patterns, the retrieval mechanism provides no benefit or introduces noise — the paper's temporal distribution shift experiments (Table IX) show gains even with 25% training data, but these could partly reflect the adapter's regularization effect rather th
The framework's value proposition scales with database size: as the historical repository grows, retrieval quality should improve (more candidates to match against) but online similarity computation cost grows linearly with N. The paper does not discuss approximate nearest neighbor search or sublinear retrieval, which would be necessary for deployment at scale with large historical databases.
The separation of offline candidate encoding from online query encoding means the candidate encoder and query encoder could in principle use different architectures or even different modalities, suggesting potential extensions to cross-domain retrieval where historical patterns from a data-rich domain supplement imputation in a data-poor domain.
The STL-decomposed positive construction implicitly assumes that the deterministic components (trend + seasonal) are the structurally relevant signal for retrieval — if the residual component carries imputation-relevant information (e.g., regime-specific noise patterns that recur), this construction would systematically miss it.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: ALER-TI applies retrieval-augmentation to time series imputation, which is a genuinely new combination. The efficiency angle — caching candidate embeddings offline and doing alignment in latent space instead of re-encoding candidates per query — is the real contribution. But the paper's central mechanism (post-hoc latent masking) is never formally specified, and the ablations don't isolate it from the candidate-guided cross-attention that's bundled into the same module. That's a load-bearing gap in the evidence chain. I'd send it to review — the empirical work is extensive enough to take seriously — but the reviewers need to push hard on the formalization and the missing ablation. Here's the detail. What's new and good: The problem formulation is well-motivated. Retrieval for forecasting has been done, but the corrupted-query-vs-clean-candidate mismatch is specific to imputation and the paper identifies it clearly. The framework is genuinely model-agnostic — they show gains on seven different backbones across three architectural families, plus two imputation-specialized methods. The experimental scope is solid: six datasets, four missing rates, four sequence lengths, multiple ablations. The efficiency argument (Table VII) is the right argument to make, and Table VIII backs it up empirically with real latency numbers. The temporal distribution shift experiment (Table IX) and the synthetic rare-pattern experiment (Table X) are thoughtful additions that address the obvious 'what if the training pool isn't relevant' concern. The contrastive training setup with STL-decomposed positives and hard negative mining is well-designed. Where it's soft: The stress-test concern lands. The LEA module bundles two mechanisms: (1) post-hoc masking of cached candidate representations and (2) candidate-guided query encoding via cross-attention (Eqs. 4–5). Table III ablates mechanism (2) — removing it hurts. But there is no corresponding ablation that keeps (2) and removes (1). So we cannot tell whether the masking step contributes anything beyond what cross-attention already provides. This matters because the masking is the paper's headline contribution and the basis for the complexity claim. Worse, the masking procedure itself is never formalized. §III.C.2 says the mask interaction is 'postponed to the latent stage,' and §III.D.4 mentions 'the post-hoc masking procedure,' but no equation shows how M is applied to the cached representation z_ci. If masking is just zeroing latent dimensions corresponding to missing positions, that's a weak operation; if it involves learned transformations, the complexity advantage may not hold as stated. The LEA≈OM result in Table II (differences of 0.000–0.002 MSE) could be fully explained by the cross-attention compensating for the lack of input-level masking, rather than by post-hoc masking providing genuine alignment. The reader's other concerns are more minor. No standard deviations in the main tables is a real but fixable issue — they ran three seeds, so the data exists. The LEA-vs-OM trade-off discussion could be sharper, but the complexity table does most of the work. Who this is for: Researchers working on time series imputation and retrieval-augmented methods. A reader who wants to understand how to adapt retrieval mechanisms to corrupted-query settings will get value from the framework design, even if the masking formalization needs work. The paper deserves a serious referee. The empirical breadth is real, the efficiency contribution is real, and the missing pieces (formalization, one ablation) are the kind that authors can address in revision. If the reviewers confirm that the masking mechanism is what it sounds like — a simple latent-dimension zeroing — then the framing needs adjustment but the framework still works. If the authors can add the missing ablation and it turns out masking doesn't matter, the contribution narrows to the cross-attention retrieval design, which is still publish

Referee Report

3 major / 8 minor

Summary. This paper proposes ALER-TI, a retrieval-augmented framework for time series imputation. The core contribution is Latent Embedding Alignment (LEA), which mitigates the representation mismatch between corrupted queries and clean historical candidates by applying post-hoc masking in the latent space rather than at the input level. This design allows candidate embeddings to be pre-computed and cached offline, avoiding the online re-encoding bottleneck of on-the-fly masking (OM) while maintaining alignment between corrupted queries and complete candidates. The framework is model-agnostic, integrating retrieved historical patterns with frozen backbone imputation models via a lightweight gated adapter. Experiments span six datasets, ten baselines, four missing rates, and four sequence lengths, with additional ablation studies on retrieval mechanisms, fusion strategies, component contributions, candidate count sensitivity, computational complexity, temporal distribution shift, and rare-pattern robustness.

Significance. The paper addresses a well-motivated problem: retrieval-augmented imputation is fundamentally different from retrieval-augmented forecasting because the query itself is corrupted, creating a representation mismatch. The LEA mechanism is a reasonable solution to the efficiency-alignment trade-off between asymmetric retrieval (AR) and on-the-fly masking (OM). The experimental evaluation is extensive, covering diverse backbones and settings. The inclusion of complexity analysis (Table VII), empirical efficiency comparison (Table VIII), distribution-shift robustness (Table IX), and synthetic rare-pattern evaluation (Table X) goes beyond standard imputation benchmarks. The framework is model-agnostic and the adapter introduces minimal trainable parameters (0.0022M). Code is provided via anonymous repository. The central claim—that post-hoc latent masking achieves comparable performance to OM while preserving cached retrieval—is falsifiable and supported by the ablation in Table II, though with important caveats noted below.

major comments (3)

§III.C.2 and §III.D.4: The post-hoc masking procedure is the paper's core contribution, yet it is never formally specified. §III.C.2 states that 'the interaction with the query mask is postponed to the latent stage,' and §III.D.4 mentions 'the post-hoc masking procedure' in the context of contrastive training, but no equation shows how the mask M is applied to the cached candidate representation z_ci (Eq. 3). The complexity argument in Table VII (online candidate encoding O(NLr'd) instead of O(NL²r'²d)) depends on what the masking operation actually does. If masking involves a learned transformation, the complexity claim may not hold; if it is simple element-wise zeroing of latent dimensions, its effectiveness is questionable and should be justified. This is load-bearing for the central claim and must be formalized with an explicit equation.
Table III and §IV.C.2: The paper bundles two distinct mechanisms in LEA—(1) post-hoc latent masking and (2) candidate-guided query encoding via cross-attention (Eqs. 4–5). Table III ablates mechanism (2) while keeping masking, showing that removing candidate-guided encoding degrades performance. However, there is no corresponding ablation that keeps candidate-guided encoding and removes post-hoc masking. Without this, one cannot determine whether the masking step contributes anything beyond what cross-attention already provides. The LEA≈OM result in Table II (differences of 0.000–0.002 MSE) could potentially be explained by candidate-guided encoding alone. A 'LEA without masking' row in Table II or Table III would isolate the contribution of the paper's titular mechanism.
Table II: The comparison between OM and ALER-TI shows differences of 0.000–0.002 in MSE on ETTh1 and 0.001–0.002 on Weather. Given that §IV.A.4 states experiments use three random seeds with averages reported, the absence of standard deviations makes it impossible to assess whether these differences are statistically meaningful. The claim that LEA 'achieves performance comparable to or better than OM' is central to the paper's contribution and needs to be supported by variance estimates or significance tests. Without them, the LEA≈OM equivalence claim is not fully supported by the evidence.

minor comments (8)

Tables XI and XII: Despite §IV.A.4 stating three random seeds are used, no standard deviations or confidence intervals are reported in any table. Adding error bars or significance tests would strengthen the empirical claims throughout.
Table I: The entry '0.00' for Helix on ETTh1 at r=0.25 is ambiguous—it could mean zero improvement or marginal degradation. Clarification would help.
§III.D.2, Eq. (8): The positive reference p+ = Y_tr + Y_se excludes the residual component. The justification is that this 'encourages LEA to focus on stable temporal structures,' but no ablation is provided to show that this choice outperforms using Y directly. A brief note on sensitivity would be useful.
§III.E.2, Eq. (14): The gating matrix g = σ(MLP_g(z_bb)) depends only on the backbone output, not on the retrieved candidates. This means the gate cannot adapt to retrieval quality. A brief discussion of whether incorporating z_re into the gating MLP was considered would clarify the design choice.
Table VIII: The 'Const. RDB Time (s)' column header is unclear. It appears to refer to the one-time cost of constructing the retrieval database, but the label should be expanded for clarity.
§IV.A.3: The temperature parameter is initialized as τ = ln(1/0.07) and learned during training. This follows the CLIP initialization, but no citation is provided. A brief reference or citation would help.
Fig. 2: The 'Masking' box in the LEA pipeline diagram is not clearly connected to any specific operation. This is consistent with the lack of formalization noted in the major comments, but the figure should at least indicate where masking is applied.
References [7], [19], [20], [25], [27]: Several references appear to be from 2026 and should be verified as properly cited and accessible.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for a careful and constructive review. All three major comments identify genuine gaps in the manuscript that we will address in revision. Below we respond point by point.

read point-by-point responses

Referee: §III.C.2 and §III.D.4: The post-hoc masking procedure is the paper's core contribution, yet it is never formally specified. No equation shows how the mask M is applied to the cached candidate representation z_ci (Eq. 3). The complexity argument depends on what masking actually does. If it is simple element-wise zeroing, its effectiveness should be justified.

Authors: The referee is correct that the post-hoc masking operation is not formally specified, and this is a significant omission given its centrality to the contribution. To clarify: the post-hoc masking is element-wise application of the query mask M (projected into the latent space) to the cached candidate representation z_ci. Concretely, we compute a latent-space mask M_lat derived from the input mask M, and the aligned candidate representation is z_ci^{aligned} = M_lat ⊙ z_ci. This is a simple element-wise operation, which is why the online candidate encoding complexity in Table VII is O(NLr'd) rather than the quadratic O(NL²r'²d) incurred by OM (which re-encodes the full masked sequence through the Transformer). We will add an explicit equation for this operation in §III.C.2 and clarify that M_lat is obtained by mapping the binary input mask M through the same positional embedding and patching used by the encoder, so that latent dimensions corresponding to missing input positions are zeroed. Regarding the effectiveness of simple element-wise zeroing: the key insight is that the candidate representation z_ci is produced by a mask-agnostic encoder, so the latent dimensions are aligned with input positions. Zeroing the latent dimensions corresponding to missing positions brings the candidate representation into the same observed subspace as the corrupted query representation, reducing the distributional mismatch that AR suffers from. This is not meant to be a sophisticated transformation; its value lies in being cheap enough to apply online while still providing meaningful alignment, as the LEA vs. AR comparison in Table II demonstrates (e.g., 0.041 vs. 0.044 MSE on ETTh1/ModernTCN, 0.038 vs. 0.042 on Weather/DLinear). We will also add a brief justification paragraph for why轻 revision: no
Referee: Table III and §IV.C.2: No ablation isolates the contribution of post-hoc masking. Table III removes candidate-guided encoding while keeping masking, but there is no corresponding ablation keeping candidate-guided encoding and removing masking. A 'LEA without masking' row would isolate the contribution of the paper's titular mechanism.

Authors: This is a fair and important point. The current ablation in Table III shows that candidate-guided query encoding contributes meaningfully (3.66–15.22% MSE improvement), but without the complementary ablation—keeping candidate-guided encoding and removing post-hoc masking—we cannot isolate the contribution of masking itself. We agree this is needed and will add a 'LEA without post-hoc masking' row to Table II (or Table III) in the revision. In this ablation, the candidate representation z_ci would be used directly without applying the latent mask M_lat, while the candidate-guided query encoding (Eqs. 4–5) and all other components remain unchanged. This will allow readers to assess whether the masking step contributes beyond what cross-attention-based interaction already provides. We note that the referee's concern—that the LEA≈OM result could be explained by candidate-guided encoding alone—is plausible and we want the experiment to speak to it directly. If the ablation shows that masking contributes little on its own, we will adjust the framing accordingly and be transparent about which mechanism drives the gains. revision: yes
Referee: Table II: Differences between OM and ALER-TI are 0.000–0.002 MSE. Three random seeds are used but no standard deviations are reported. The LEA≈OM equivalence claim needs variance estimates or significance tests.

Authors: The referee is correct. With differences of 0.000–0.002 MSE and three seeds, the absence of standard deviations makes the equivalence claim unsupported. We will add standard deviations to Table II (and to Table III, which also reports single-number averages). If the standard deviations turn out to be comparable to or larger than the OM–LEA differences, we will revise the claim from 'comparable to or better than OM' to a more precise statement, e.g., 'LEA achieves performance within the noise margin of OM while reducing online candidate encoding from O(NL²r'²d) to O(NLr'd).' This would still support the paper's core argument—that LEA preserves OM-level alignment quality at substantially lower computational cost—but the equivalence claim would be stated with appropriate statistical caution. We will also consider running additional seeds (e.g., 5 or 10) to tighten the estimates if the current variance is too large to draw conclusions from three seeds. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: ALER-TI's derivation is self-contained against external benchmarks

full rationale

The paper's central claim—that LEA provides a favorable trade-off between retrieval quality and computational efficiency—is grounded in independently measurable quantities. The LEA module is trained with a contrastive InfoNCE objective (Eq. 10) on training data, and the adapter is trained with MSE on missing entries (Eq. 16). Performance is measured against external baselines (ModernTCN, TimesNet, SAITS, etc.) from prior literature, with ablations comparing against random retrieval, metric-based retrieval, and alternative fusion strategies (Tables II–V). The complexity analysis (Table VII) is derived from standard Transformer complexity formulas. No step in the derivation chain reduces to its own inputs by construction. The LEA≈OM comparison in Table II is an empirical result, not a definitional identity. While the post-hoc masking procedure is informally specified (noted in the skeptic's analysis), this is a correctness/formalization concern, not circularity. The paper does not fit a parameter to a subset of data and then predict that same subset, nor does it invoke self-citations as load-bearing evidence for its core mechanism. The framework's value proposition is tested against external benchmarks and ablation baselines, making the derivation self-contained.

Assumptions & free parameters 5 free parameters · 4 assumptions · 3 invented entities

The axiom ledger captures the key assumptions and design choices in ALER-TI. The free parameters are standard hyperparameters with reasonable defaults. The domain assumptions are clearly stated in the methodology. The invented entities (LEA, candidate-guided encoding, AF adapter) are all supported by ablation studies with independent evidence.

free parameters (5)

m (number of query codes) = 16
Chosen as a hyperparameter; stated as m << L. Not tuned per dataset in the main experiments.
d (latent dimension) = 64
Fixed across experiments.
τ (temperature) = ln(1/0.07) initial, learned
Initialized to a specific value and then learned during training.
k (top-k candidates) = 3
Selected based on sensitivity analysis in Table VI; fixed as default for all experiments.
Learning rate = 0.001
Fixed for LEA training.

assumptions (4)

domain assumption Historical subsequences from the training split contain structurally relevant patterns for reconstructing missing values in test queries.
Invoked in §III.B and §III.E.1 where the candidate pool D_train is constructed from training data S. The entire retrieval framework depends on this assumption.
domain assumption STL decomposition separates deterministic structure from stochastic fluctuations, and the deterministic components (trend + seasonal) are the correct positive references for contrastive learning.
Invoked in §III.D.2, Eq. 7-8. The choice of p+ = Y_tr + Y_sea as the positive sample is a modeling assumption about what the retriever should focus on.
domain assumption Pearson correlation is an appropriate metric for selecting hard negatives.
Invoked in §III.D.3, Eq. 9. Used to identify candidates that are 'close enough to challenge the retriever.'
domain assumption The frozen backbone output z_bb and the retrieved representation z_re can be meaningfully combined via a learned gating matrix.
Invoked in §III.E.2, Eqs. 13-14. Assumes that the backbone and retrieval representations live in a compatible space that RevIN + gating can align.

invented entities (3)

Latent Embedding Alignment (LEA) module independent evidence
purpose: Aligns corrupted query representations with cached clean candidate representations via post-hoc latent masking and candidate-guided query encoding.
The module is evaluated against multiple alternatives (random, Pearson, DTW, AR, OM) in Table II, showing independent evidence of effectiveness. Its complexity is analyzed in Table VII and compared empirically in Table VIII.
Candidate-guided query encoding (multi-view extractor with learnable query codes) independent evidence
purpose: Summarizes observed query context into m contextual vectors and aggregates them using candidate representations as guidance.
Ablated in Table III, showing consistent degradation when removed across all datasets.
ALER-TI Fusion (AF) adapter independent evidence
purpose: Fuses frozen backbone output with retrieved references via gating and residual MLP.
Compared against Linear Fusion and Cross-attention Fusion in Table IV, showing consistent superiority.

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Cite this review

Pith. "Pith review of ALER-TI: Aligned Latent Embedding Retrieval for Time Series Imputation." pith.science (2026). https://pith.science/paper/PRVOPX3V

@misc{pith2026260707640,
  author       = {Pith},
  title        = {Pith review of: ALER-TI: Aligned Latent Embedding Retrieval for Time Series Imputation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PRVOPX3V}},
  note         = {Machine review of arXiv:2607.07640}
}

read the original abstract

Deep learning has significantly advanced time series imputation, yet most existing architectures primarily rely on localized temporal context within the corrupted input sequence. This reliance can be limiting in real-world scenarios, where time series often exhibit non-stationary dynamics, weak temporal correlations, and infrequent patterns that are difficult to reconstruct from nearby observations alone. In this paper, we propose ALER-TI, Aligned Latent Embedding Retrieval for Time Series Imputation, a retrieval-augmented framework that explicitly leverages historical patterns to supplement degraded local context for more reliable missing-value reconstruction. The core of ALER-TI is Latent Embedding Alignment (LEA), which mitigates the representation mismatch between corrupted queries and complete historical candidates. By applying post-hoc masking in the latent space, LEA aligns candidates with the query's missingness pattern while allowing historical embeddings to be pre-computed and cached for efficient retrieval. ALER-TI is model-agnostic and can be integrated with various imputation backbones through a lightweight adaptation module. Extensive experiments on six real-world datasets under different missing rates demonstrate that ALER-TI consistently improves strong baseline models and enhances robustness across diverse imputation settings.

Figures

Figures reproduced from arXiv: 2607.07640 by the authors.

**Figure 1.** Comparison of retrieval strategies for time-series imputation. (a) Asymmetric Retrieval (AR): Encodes the query and candidates independently, with candidates represented as fully observed trajectories. (b) On-the-fly Masking (OM): Applies masking before encoding to align candidate representations with the query pattern, but requires online re-encoding at inference time. (c) LEA (Ours): Uses mask-agnostic encoding a… view at source ↗

**Figure 2.** Overview of the ALER-TI pipeline. (Left) LEA aligns corrupted queries with clean candidates through late-stage masking and candidate-guided query encoding. (Right) The retrieved historical signals are integrated with the output of a frozen backbone model through lightweight fusion, followed by a MLP-based block for refined reconstruction. C. Latent Embedding Alignment (LEA) 1) LEA Architecture: The Latent Embedding … view at source ↗

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ModernTCN: A modern pure convolution structure for general time series analysis,

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2024
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Spectral text fusion: A frequency-aware approach to multimodal time series forecasting,

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2023
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2021
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2026
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2023
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Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting,

Y . Zhang and J. Yan, “Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting,” inProc. ICLR, 2023

2023
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Informer: Beyond efficient transformer for long sequence time series forecasting,

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2021

Pith tools

Reviewed July 9, 2026 · model on record in the stance chip above.

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