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arxiv: 2604.24041 · v1 · submitted 2026-04-27 · 💻 cs.LG · cs.AI

End-to-End Learning for Partially-Observed Time Series with PyPOTS

Wenjie Du , Yiyuan Yang , Tianxiang Zhan , Qingsong Wen This is my paper

Pith reviewed 2026-05-08 04:35 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords partially observed time seriesend-to-end machine learningmissing data imputationtime series forecastingclassificationanomaly detectionPython library
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The pith

PyPOTS supplies one Python ecosystem that keeps missing-value handling inside the full machine learning pipeline for incomplete time series.

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

Most existing setups split the work of filling gaps in time series data from the later steps of training models for prediction or classification, which can reduce consistency across experiments. This paper presents PyPOTS as a single open-source library that bundles simulation of missing entries, preprocessing, model training, and evaluation into connected workflows. It supports the main tasks of imputation, forecasting, classification, clustering, and anomaly detection through one set of interfaces. The library includes ready-to-run examples for everyday use and clear instructions for adding new models or constraints, so that research and production pipelines stay reproducible without switching between separate pieces of software.

Core claim

The paper presents PyPOTS as an open-source Python ecosystem built for end-to-end data mining and machine learning on partially-observed time series. It supplies unified APIs and benchmark-style experiments that cover missingness simulation, data preprocessing, model training, and performance evaluation for imputation, forecasting, classification, clustering, and anomaly detection, with separate guidance for practitioners who apply the tools and for developers who extend them with custom models or domain rules.

What carries the argument

The PyPOTS unified API that folds missing-value simulation and handling directly into the training and evaluation steps for downstream tasks instead of treating them as separate stages.

If this is right

  • Practitioners obtain complete, traceable pipelines that reduce the chance of inconsistent handling between data cleaning and model training.
  • Benchmark-oriented experiments become easier to repeat and compare across different models for the same POTS tasks.
  • Developers can add custom models or domain constraints inside the same framework without rebuilding the surrounding workflow.
  • Production systems gain reusable code paths that keep missing-data logic and learning logic in one place.

Where Pith is reading between the lines

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

  • The integrated structure could let models learn patterns of missingness as part of their optimization rather than treating gaps only as a preprocessing fix.
  • A shared codebase might make it simpler to create community-wide test suites that measure how well different methods cope with varying rates of missing data.
  • The same design pattern could be tested on other data types that arrive with gaps, such as spatial or graph data, to check whether end-to-end handling generalizes.

Load-bearing premise

That a single library with shared APIs and benchmark experiments will produce higher reproducibility and better task performance than the current practice of using different tools for missing values and for learning.

What would settle it

A head-to-head test on standard partially-observed time series datasets in which separate toolchains achieve equal or higher reproducibility and accuracy numbers than PyPOTS pipelines.

read the original abstract

Partially-observed time series (POTS) is ubiquitous in real-world applications, yet most existing toolchains separate missing-value handling from downstream learning, which limits reproducibility and overall performance. This tutorial introduces PyPOTS, an open-source Python ecosystem for end-to-end data mining and machine learning on POTS. We present practical workflows spanning missingness simulation, data preprocessing, model training, and evaluation across core tasks, including imputation, forecasting, classification, clustering, and anomaly detection. The tutorial consists of two parts: Part I emphasizes hands-on application for practitioners through unified APIs and benchmark-oriented experiments. Part II targets developers and researchers, focusing on extending PyPOTS with custom models, domain-specific constraints, and contribution-ready engineering practices. Participants will gain both conceptual understanding and implementation experience for building robust, transparent, and reusable POTS pipelines in research and production settings. PyPOTS is publicly available at https://github.com/WenjieDu/PyPOTS

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

1 major / 2 minor

Summary. The manuscript introduces PyPOTS, an open-source Python ecosystem for end-to-end data mining and machine learning on partially-observed time series (POTS). It outlines practical workflows for missingness simulation, preprocessing, training, and evaluation across imputation, forecasting, classification, clustering, and anomaly detection tasks. The tutorial is structured in two parts: Part I for practitioners using unified APIs and benchmark experiments, and Part II for developers extending the package with custom models and engineering practices.

Significance. A well-documented, unified open-source toolkit with reproducible workflows could meaningfully improve accessibility and standardization for POTS research if the motivating claim holds. However, the manuscript provides no empirical comparisons or metrics demonstrating gains in reproducibility or performance over separated toolchains, limiting its immediate research impact to that of software documentation.

major comments (1)
  1. [Abstract] Abstract: the assertion that separation of missing-value handling from downstream learning 'limits reproducibility and overall performance' is stated as background motivation but is unsupported by any experiments, benchmarks, tables, or derivations in the manuscript. This claim is load-bearing for the tutorial's rationale yet remains untested.
minor comments (2)
  1. The benchmark-oriented experiments section should include concrete metrics (e.g., MAE, RMSE, or reproducibility scores) and baseline comparisons to allow readers to evaluate the end-to-end approach.
  2. Code snippets and API examples would benefit from explicit notes on dependencies, expected outputs, and handling of edge cases for missingness patterns to enhance reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review of our tutorial manuscript introducing PyPOTS. We address the single major comment below and outline the corresponding revision.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion that separation of missing-value handling from downstream learning 'limits reproducibility and overall performance' is stated as background motivation but is unsupported by any experiments, benchmarks, tables, or derivations in the manuscript. This claim is load-bearing for the tutorial's rationale yet remains untested.

    Authors: We agree that the manuscript, which is a tutorial on the PyPOTS toolkit rather than a research paper presenting new benchmarks, does not include experiments, tables, or derivations quantifying the claimed limitations of separated toolchains. The statement is intended as background motivation drawn from observed practical challenges in the POTS literature. To address this concern directly, we will revise the abstract to present the point as a motivation based on workflow inconsistencies rather than an empirical assertion, and we will incorporate supporting references to prior work on integrated time-series pipelines in the introduction section. revision: yes

Circularity Check

0 steps flagged

No significant circularity in descriptive tutorial

full rationale

The manuscript is a tutorial and software release note for the PyPOTS Python package. It describes workflows for imputation, forecasting, classification, clustering, and anomaly detection on partially-observed time series using unified APIs, without any equations, derivations, predictions, fitted parameters, or load-bearing self-citations. All content is expository and self-contained; no step reduces to its own inputs by construction or renames a result as a novel finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a software tutorial paper; no mathematical derivations, fitted parameters, axioms, or invented entities are present.

pith-pipeline@v0.9.0 · 5472 in / 1076 out tokens · 34021 ms · 2026-05-08T04:35:08.218954+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages

  1. [1]

    Deepak Adhikari, Wei Jiang, Jinyu Zhan, Zhiyuan He, Danda B Rawat, Uwe Aickelin, and Hadi A Khorshidi. 2022. A comprehensive survey on imputation of missing data in internet of things.Comput. Surveys55, 7 (2022), 1–38

    work page 2022

  2. [2]

    Svetlana Bryzgalova, Sven Lerner, Martin Lettau, and Markus Pelger. 2025. Miss- ing financial data.The Review of Financial Studies38, 3 (2025), 803–882

    work page 2025

  3. [3]

    Wenjie Du, Jun Wang, Linglong Qian, Yiyuan Yang, Zina Ibrahim, Fanxing Liu, Zepu Wang, Haoxin Liu, Zhiyuan Zhao, Yingjie Zhou, et al . 2024. Tsi-bench: Benchmarking time series imputation.arXiv preprint arXiv:2406.12747(2024)

    work page arXiv 2024

  4. [4]

    Wenjie Du, Yiyuan Yang, Linglong Qian, Jun Wang, and Qingsong Wen. 2023. PyPOTS: A Python Toolkit for Machine Learning on Partially-Observed Time Series.arXiv e-prints(2023), arXiv–2305

    work page 2023

  5. [5]

    Maksims Kazijevs and Manar D Samad. 2023. Deep imputation of missing values in time series health data: A review with benchmarking.Journal of biomedical informatics144 (2023), 104440

    work page 2023

  6. [6]

    Mourad Khayati, Alberto Lerner, Zakhar Tymchenko, and Philippe Cudré- Mauroux. 2020. Mind the gap.Proceedings of the VLDB Endowment13 (2020), 768–782

    work page 2020

  7. [7]

    Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume, Christopher Holder, David Guijo-Rubio, Guzal Bulatova, Leonidas Tsaprounis, Lukasz Mentel, Martin Walter, Patrick Schäfer, et al. 2024. aeon: a Python toolkit for learning from time series.Journal of Machine Learning Research25, 289 (2024), 1–10

    work page 2024

  8. [8]

    Steffen Moritz and Thomas Bartz-Beielstein. 2017. imputeTS: time series missing value imputation in R. (2017)

    work page 2017

  9. [9]

    Quentin Nater, Mourad Khayati, and Philippe Cudré-Mauroux. 2025. A Hands- on Tutorial on Time Series Imputation with ImputeGAP. InProceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2. 6245–6246

    work page 2025

  10. [10]

    Jun Wang, Wenjie Du, Yiyuan Yang, Linglong Qian, Wei Cao, Keli Zhang, Wenjia Wang, Yuxuan Liang, and Qingsong Wen. 2025. Deep learning for multivariate time series imputation: a survey. InProceedings of the 34th IJCAI(Montreal, Canada)(IJCAI ’25). Article 1187, 9 pages. doi:10.24963/ijcai.2025/1187

    work page doi:10.24963/ijcai.2025/1187 2025

  11. [11]

    Kai Zhang, Qinmin Yang, Chao Li, Xin Sun, and Jiming Chen. 2025. Missing data recovery methods on multivariate time series in iot: A comprehensive survey. IEEE Communications Surveys & Tutorials(2025)

    work page 2025

  12. [12]

    Yimei Zhang, Xiangjie Kong, Wenfeng Zhou, Jin Liu, Yanjie Fu, and Guojiang Shen. 2024. A comprehensive survey on traffic missing data imputation.IEEE Transactions on Intelligent Transportation Systems25, 12 (2024), 19252–19275

    work page 2024