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Diffusion-based Time Series Data Imputation for Microsoft 365

arXiv 2309.02564 ↗First posted 3 Aug 2023cs.DC cs.AI cs.LGcited by 0

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Abstract (arXiv)

Reliability is extremely important for large-scale cloud systems like Microsoft 365. Cloud failures such as disk failure, node failure, etc. threaten service reliability, resulting in online service interruptions and economic loss. Existing works focus on predicting cloud failures and proactively taking action before failures happen. However, they suffer from poor data quality like data missing in model training and prediction, which limits the performance. In this paper, we focus on enhancing data quality through data imputation by the proposed Diffusion+, a sample-efficient diffusion model, to impute the missing data efficiently based on the observed data. Our experiments and application practice show that our model contributes to improving the performance of the downstream failure prediction task.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 38 references that are in the map. First posted on arXiv 3 Aug 2023.

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13 of 38 references.

References · 13

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  5. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,961
  6. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 577
  7. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 548
  8. 2018BRITS: Bidirectional Recurrent Imputation for Time Seriescited by 288
  9. 2021Gotta Go Fast When Generating Data with Score-Based Modelscited by 218
  10. 2022Diffusion-based Time Series Imputation and Forecasting with Structured State Space Modelscited by 195
  11. 2019GP-VAE: Deep Probabilistic Time Series Imputationcited by 171
  12. 2019NAOMI: Non-Autoregressive Multiresolution Sequence Imputationcited by 59
1 more
  1. 2018Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Predictioncited by 2

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  1. 2025Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification
  2. 2023MissDiff: Training Diffusion Models on Tabular Data with Missing Values
  3. 2026Analyzing and Improving Diffusion Models for Time-Series Data Imputation: A Proximal Recursion Perspective
  4. 2025Boundary-enhanced time series data imputation with long-term dependency diffusion models
  5. 2025Self-attention-based Diffusion Model for Time-series Imputation in Partial Blackout Scenarios
  6. 2026Discretizing Continuous Time Series for Imputation with Masked Diffusion Training
  7. 2024SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputation
  8. 2026RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation