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arxiv: 2605.16069 · v1 · pith:PJIYTXECnew · submitted 2026-05-15 · 💻 cs.LG

ITGPT: Generative Pretraining on Irregular Timeseries

Antoine Honor\'e , Ming Xiao This is my paper

Pith reviewed 2026-05-20 20:39 UTC · model grok-4.3

classification 💻 cs.LG
keywords irregular timeseriesgenerative pretrainingself-supervised learningmultimodal dataattention mechanismshealthcare predictionpredictive maintenance
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The pith

ITGPT enables generative pretraining directly on irregular multimodal timeseries data.

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

The paper presents ITGPT as an attention-based model that trains on raw irregular timeseries from multiple sources using self-supervised learning and generative pretraining objectives. It targets settings like healthcare and equipment monitoring where data arrives at uneven intervals and labels are costly to obtain. The approach avoids the usual steps of resampling the data, fusing features by hand, or filling in missing values. A reader would care because the method suggests a route to training useful predictors from the large volumes of messy sensor data that already exist, rather than waiting for perfectly cleaned and labeled sets.

Core claim

ITGPT is an attention-based architecture designed for handling multimodal, irregularly sampled timeseries by allowing training with both SSL losses and GPT-like objectives. It achieves state-of-the-art performance on the TIHM healthcare dataset and the CompX predictive maintenance dataset without requiring resampling, feature fusion or explicit data imputation. When labels are scarce, ITGPT effectively leverages unlabeled data through SSL and GPT training, outperforming the purely supervised approach.

What carries the argument

The ITGPT attention-based architecture that ingests raw irregular multimodal timeseries and trains end-to-end with self-supervised and generative pretraining losses.

If this is right

  • State-of-the-art results on healthcare regression tasks with the TIHM dataset using irregular multimodal inputs.
  • State-of-the-art results on predictive maintenance tasks with the CompX dataset without preprocessing steps.
  • Improved accuracy over purely supervised training when only a small fraction of the data carries labels.
  • Direct use of existing unlabeled sensor streams without resampling or explicit missing-value handling.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same pretraining recipe could be tested on irregular data from environmental sensors or financial tick streams where labels are also sparse.
  • If the architecture scales, it reduces the engineering effort spent on domain-specific cleaning pipelines before modeling.
  • One could measure whether adding more unlabeled streams from additional modalities continues to lift downstream accuracy without extra labels.

Load-bearing premise

An attention-based architecture can be trained end-to-end on raw irregular multimodal timeseries using only SSL and GPT-like objectives and still produce accurate downstream predictions.

What would settle it

On the TIHM dataset with limited labels, a version of ITGPT trained only with standard supervised loss on imputed data matches or exceeds the performance of the full SSL-plus-GPT version.

Figures

Figures reproduced from arXiv: 2605.16069 by Antoine Honor\'e, Ming Xiao.

Figure 1
Figure 1. Figure 1: ITGPT architecture description. The encoder and decoders are two ITNet models. X and Z are multimodal data and the [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Causal cross-attention in ITNet. We use additive [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 1
Figure 1. Figure 1: with additional prediction and embedding transforms in [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Histogram of average time deltas between samples of [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Results in a 5-fold cross-validation framework on the CompX dataset. Short Description Symbol Value Fixed key/queries dimensions dk 32 key/query/value maps fQ/K/V Linear Activation function φ ReLU Anchor dimension da 64 Batch size B 64 Optimizer / ADAM Learning rate λ 5 × 10−4 Number of epochs Ne 20 Varied Chain depth L {1, 2, 3, 4, 5, 6, 7} Dropout probability p {0, 0.1, 0.2, 0.3} Mixing layer W {Linear, … view at source ↗
Figure 5
Figure 5. Figure 5: Recall (Sensitivity) and Specificity for an increasing [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

Timeseries regression models often struggle to leverage large volumes of labeled multimodal data, particularly when the data are irregularly sampled or contain missing values. This is common in domains like healthcare and predictive maintenance, where data are collected from unreliable sources, and labeling requires expert knowledge or costly equipments. Transformer-based large language models have proven effective on structured data such as text through self-supervised learning (SSL) and generative pretraining (GPT) frameworks. However, such models lack the flexibility to efficiently process irregularly sampled multimodal timeseries data. In this paper, we introduce ITGPT, an attention-based architecture designed for handling multimodal, irregularly sampled timeseries by allowing training with both SSL losses and GPT-like objectives. We evaluate its performance on a healthcare task with the TIHM dataset, and a predictive maintenance task with the CompX dataset. Our results demonstrate that ITGPT achieves state-of-the-art performance without requiring resampling, feature fusion or explicit data imputation. Furthermore, when labels are scarce, ITGPT effectively leverages unlabeled data through SSL and GPT training, outperforming the purely supervised approach. This represents an important step towards efficiently using large and unstructured timeseries datasets for practical inference tasks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces ITGPT, an attention-based architecture for multimodal irregularly sampled timeseries. It supports end-to-end training via self-supervised learning (SSL) losses and GPT-like generative objectives without resampling, explicit imputation, or feature fusion. Evaluations on the TIHM healthcare dataset and CompX predictive maintenance dataset claim state-of-the-art results; in low-label regimes the model is said to outperform purely supervised baselines by leveraging large amounts of unlabeled data through pretraining.

Significance. If the performance gains are robust and attributable to the pretraining objectives rather than architecture capacity, the work would offer a practical route for applying generative pretraining to irregular multimodal timeseries in label-scarce domains. This could reduce reliance on costly preprocessing steps common in healthcare and maintenance applications. The approach extends successful NLP pretraining ideas to a new data modality, but the current evidence base is too thin to assess whether the central empirical claims hold.

major comments (2)
  1. [Experiments / low-label results] Low-label regime experiments (TIHM and CompX splits): no ablation compares a pretrained ITGPT against a randomly initialized ITGPT of identical architecture and capacity trained only with the supervised objective. Without this controlled comparison the claim that 'ITGPT effectively leverages unlabeled data through SSL and GPT training, outperforming the purely supervised approach' cannot be isolated from possible differences in model capacity or regularization.
  2. [Results / Tables] SOTA claims on TIHM and CompX: the manuscript provides no error bars, statistical significance tests, or detailed baseline descriptions (hyperparameters, training budgets, or exact preprocessing for competing methods). This weakens the assertion of state-of-the-art performance without resampling or imputation.
minor comments (2)
  1. [Abstract] Abstract states performance claims but omits quantitative metrics, dataset sizes, or any mention of error bars or statistical tests.
  2. [Model description] Notation for irregular sampling and multimodal fusion is introduced without a clear diagram or pseudocode showing how raw timestamps and modalities are fed into the attention layers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and have revised the manuscript to strengthen the empirical claims.

read point-by-point responses

  1. Referee: [Experiments / low-label results] Low-label regime experiments (TIHM and CompX splits): no ablation compares a pretrained ITGPT against a randomly initialized ITGPT of identical architecture and capacity trained only with the supervised objective. Without this controlled comparison the claim that 'ITGPT effectively leverages unlabeled data through SSL and GPT training, outperforming the purely supervised approach' cannot be isolated from possible differences in model capacity or regularization.

    Authors: We agree that this controlled ablation is required to isolate the contribution of pretraining from architecture capacity. The original manuscript compared ITGPT to other supervised baselines but did not include a randomly initialized ITGPT trained only with the supervised loss. We have now run and added this exact comparison on the low-label splits of both TIHM and CompX. The new results show consistent gains from pretraining and are reported in the revised Section 4.3 and updated tables. revision: yes

  2. Referee: [Results / Tables] SOTA claims on TIHM and CompX: the manuscript provides no error bars, statistical significance tests, or detailed baseline descriptions (hyperparameters, training budgets, or exact preprocessing for competing methods). This weakens the assertion of state-of-the-art performance without resampling or imputation.

    Authors: We accept that the absence of error bars, significance tests, and baseline details weakens the SOTA claims. In the revision we have added standard deviations over five random seeds to all reported metrics, included paired t-test p-values against the strongest baseline, and expanded the experimental setup section with full hyperparameter tables, training budgets, and preprocessing descriptions for every competing method. These changes make the no-resampling/no-imputation advantage of ITGPT reproducible and statistically supported. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical performance claims rest on experimental evaluation

full rationale

The paper introduces an attention-based architecture (ITGPT) for irregular multimodal timeseries and reports empirical results on the TIHM healthcare and CompX maintenance datasets. Central claims of state-of-the-art performance without resampling/imputation and effective leverage of unlabeled data via SSL/GPT objectives in low-label regimes are presented as outcomes of training and evaluation rather than any mathematical derivation, fitted parameter renamed as prediction, or self-citation chain that reduces to the inputs by construction. No equations, uniqueness theorems, or ansatzes are invoked in a load-bearing manner; the architecture is described as designed to handle the data characteristics, with results measured against baselines. This is a standard empirical ML paper whose validity hinges on experimental controls, not tautological reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract supplies no explicit free parameters, axioms, or invented entities beyond the high-level claim that an attention-based model can process irregular multimodal series. All technical assumptions remain implicit.

pith-pipeline@v0.9.0 · 5728 in / 1154 out tokens · 53262 ms · 2026-05-20T20:39:09.151164+00:00 · methodology

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Reference graph

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