Data Science Seminar: Time Series Imputation
Lecturer: Mourad Khayati
Teaching Assistant: Quentin Nater
Level: Master (5 ECTS), Fall 2025
Location: Fribourg
Overview
The data science seminar involves presentations covering recent topics in data science. The area of this year’s seminar is time series imputation. As part of the seminar, we will study research papers that propose algorithms for imputing missing values. These papers present methods for reconstructing incomplete sensor data by applying various replacement strategies to estimate missing segments.
Imputation offers benefits on two levels. At the data processing level, the completed time series can be adequately utilized in a wide range of Machine Learning (ML) tasks, such as classification and forecasting. At the data management level, properly imputed time series can be more effectively stored and maintained, one reason why many Time Series Database Systems (TSDBs) have begun to incorporate native support for missing value imputation.
Structure
The goal for the students is to learn how to critically read and study a research paper, describe it in a report, and present it in front of an audience. Under supervision, students will select one paper to study and compare it with related work. This seminar aims to help students gather in-depth knowledge of an advanced topic and develop the skills required to describe a complex problem from the time series field in the form of both a presentation and a written report.
Students will also learn how to integrate an algorithm into a library, benchmark it against other algorithms, and perform the required unit tests. For this purpose, we will use ImputeGAP, a comprehensive library designed for time series imputation analysis.
Evaluation and Expectations
The final grade depends on the quality of the report, presentation, integration quality, and active participation during the seminar. Each participant prepares a self-contained report of at least five pages and gives a presentation of 30 minutes. The report should:
- Describe the proposed algorithm in detail
- Include small running examples or counterexamples
- Explore extreme cases where the algorithm performs best and worst
Reproducibility consists of reproducing the same set of experiments introduced in the paper using a different setup (dataset, metric, parameters, etc.).
Advice on how to:
- Write the report
- Prepare the presentation
Attendance is mandatory for the two-class seminar sessions. The total number of participants is limited to 20.
Schedule
Kickoff Meeting
Organization of the seminar: Tue, 23.09.2025, 14:15–16:00 — Room E230
Introduction Seminar Material
Library introduction and paper assignment: Tue, 07.10.2025, 14:15–16:00 — Room C421
First Presentation Session
Report deadline of Batch 1: Tue, 11.11.2025
Office meeting with students from Batch 1: Tue, 18.11.2025 (all day) — Room C433
Presentations of Batch 1: Tue, 25.11.2025, 14:15–19:00 — Room PER17, 001
Second Presentation Session
Report deadline for Batch 2: Tue, 25.11.2025
Office meeting with students from Batch 2: Tue, 02.12.2025 (all day) — Room C433
Presentations of Batch 2: Tue, 09.12.2025, 14:15–19:00 — Room PER17, 001
Deadline Final Report
Tue, 13.01.2026
Code Inspection
Tue, 20.01.2026 (all day) — Room C433
Tue, 27.01.2026 (all day) — Room C433
Paper Assignment
Papers are distributed on a first-come, first-served basis.
| Presenter | Date | Paper |
|---|---|---|
| Volodymyr Kachuriak | 25.11.2025 | SAITS: Self-attention-based imputation for time series, ESWA’23 |
| Sinthuja Vijayananthan | 25.11.2025 | Missing Value Imputation on Multidimensional Time Series, VLDB’21 |
| Daksh Patel | 25.11.2025 | BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition, ICML’24 |
| Nicolas Wyss | 25.11.2025 | Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values, ICLR’24 |
| Carla Malo | 25.11.2025 | NuwaTS: a Foundation Model Mending Every Incomplete Time Series, arXiv’24 |
| Jue Wang | 25.11.2025 | Filling the Gaps: Multivariate Time Series Imputation by Graph Neural Networks, ICLR’22 |
| Emma Chambers | 09.12.2025 | CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation, NeurIPS’21 |
| Alejandro Nardo González | 09.12.2025 | ReCTSi: Resource-efficient Correlated Time Series Imputation via Decoupled Pattern Learning and Completeness-aware Attentions, KDD’24 (no repro) |
| Sebastian Käslin | 09.12.2025 | GP-VAE: Deep probabilistic time series imputation, AISTATS’20 |
| Sanika Deore | 09.12.2025 | Networked Time Series Imputation via Position-aware Graph Enhanced VAEs, KDD’23 (no repro) |
| Flaminia Trinica | 09.12.2025 | Mining of Switching Sparse Networks for Missing Value Imputation, KDD’24 |
| Alba Adili | 09.12.2025 | TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis, ICLR’23 (no repro) |
| Ghulam Hussain Wabil | 09.12.2025 | TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis, ICLR’23 (no repro) |
Papers marked with (no repro) are exempt from the reproducibility task.