mlsanityData to 14 Oct 2026

Multi-Variate Time Series Forecasting on Variable Subsets

arXiv 2206.12626 ↗First posted 25 Jun 2022cs.LGcited by 5

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Abstract (arXiv)

We formulate a new inference task in the domain of multivariate time series forecasting (MTSF), called Variable Subset Forecast (VSF), where only a small subset of the variables is available during inference. Variables are absent during inference because of long-term data loss (eg. sensor failures) or high -> low-resource domain shift between train / test. To the best of our knowledge, robustness of MTSF models in presence of such failures, has not been studied in the literature. Through extensive evaluation, we first show that the performance of state of the art methods degrade significantly in the VSF setting. We propose a non-parametric, wrapper technique that can be applied on top any existing forecast models. Through systematic experiments across 4 datasets and 5 forecast models, we show that our technique is able to recover close to 95% performance of the models even when only 15% of the original variables are present.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 25 Jun 2022.

Builds on

17 of 37 references.

References · 17

  1. 2017Billion-scale similarity search with GPUscited by 2,497
  2. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  3. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  4. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  5. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  6. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
  7. 2020A Transformer-based Framework for Multivariate Time Series Representation Learningcited by 453
  8. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
  9. 2020Nearest Neighbor Machine Translationcited by 209
  10. 2018Temporal Pattern Attention for Multivariate Time Series Forecastingcited by 125
  11. 2020XOR QA: Cross-lingual Open-Retrieval Question Answeringcited by 121
  12. 2020Time Series Domain Adaptation via Sparse Associative Structure Alignmentcited by 51
5 more
  1. 2019Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Valuescited by 37
  2. 2019Non-Parametric Adaptation for Neural Machine Translationcited by 34
  3. 2020Learning Individual Models for Imputation (Technical Report)cited by 7
  4. 2021Applying k-nearest neighbors to time series forecasting : two new approachescited by 4
  5. 2020A network-based transfer learning approach to improve sales forecasting of new productscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 99
  3. 2024GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missingcited by 20
  4. 2025Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Ratescited by 20
  5. 2026PLGC: Pseudo-Labeled Graph Condensationcited by 0