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Time2Vec: Learning a Vector Representation of Time

arXiv 1907.05321 ↗First posted 11 Jul 2019cs.LGcited by 208

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Abstract (arXiv)

Time is an important feature in many applications involving events that occur synchronously and/or asynchronously. To effectively consume time information, recent studies have focused on designing new architectures. In this paper, we take an orthogonal but complementary approach by providing a model-agnostic vector representation for time, called Time2Vec, that can be easily imported into many existing and future architectures and improve their performances. We show on a range of models and problems that replacing the notion of time with its Time2Vec representation improves the performance of the final model.

Key facts. Cited by 208 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jul 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 19; 2022: 21; 2023: 43; 2024: 45; 2025: 40; 2026: 40.

208 papers.

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  1. 2021Multi-Time Attention Networks for Irregularly Sampled Time Seriescited by 234
  2. 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 188
  3. 2023β-Variational autoencoders and transformers for reduced-order modelling of fluid flowscited by 76
  4. 2021CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction taskscited by 75
  5. 2024FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusioncited by 67
  6. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
  7. 2022PinnerFormer: Sequence Modeling for User Representation at Pinterestcited by 57
  8. 2023GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendationcited by 57
  9. 2022Provably expressive temporal graph networkscited by 56
  10. 2022Neighborhood-aware Scalable Temporal Network Representation Learningcited by 54
  11. 2021A Review of Location Encoding for GeoAI: Methods and Applicationscited by 53
  12. 2021Long-Range Transformers for Dynamic Spatiotemporal Forecastingcited by 46
38 more
  1. 2025RelGNN: Composite Message Passing for Relational Deep Learningcited by 45
  2. 2022Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modelingcited by 33
  3. 2023Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoderscited by 32
  4. 2024EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Recordscited by 31
  5. 2021Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Timecited by 27
  6. 2023Using Sequences of Life-events to Predict Human Livescited by 27
  7. 2021Towards Generating Real-World Time Series Datacited by 26
  8. 2024Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Datacited by 25
  9. 2025Relational Deep Learning: Challenges, Foundations and Next-Generation Architecturescited by 22
  10. 2024A survey of dynamic graph neural networkscited by 21
  11. 2024CORE-BEHRT: A Carefully Optimized and Rigorously Evaluated BEHRTcited by 20
  12. 2023T-Rep: Representation Learning for Time Series using Time-Embeddingscited by 19
  13. 2021Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influencescited by 18
  14. 2022TIGGER: Scalable Generative Modelling for Temporal Interaction Graphscited by 18
  15. 2023It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformerscited by 18
  16. 2024TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Expertscited by 18
  17. 2024Efficient Neural Common Neighbor for Temporal Graph Link Predictioncited by 18
  18. 2024On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functionscited by 16
  19. 2025Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Seriescited by 16
  20. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
  21. 2022Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Surveycited by 14
  22. 2021Neural Predicting Higher-order Patterns in Temporal Networkscited by 13
  23. 2023On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Datacited by 13
  24. 2023RDGSL: Dynamic Graph Representation Learning with Structure Learningcited by 13
  25. 2024TrajGPT: Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal Modelcited by 13
  26. 2025GeoGen: A Two-stage Coarse-to-Fine Framework for Fine-grained Synthetic Location-based Social Network Trajectory Generationcited by 12
  27. 2025GT-Loc: Unifying When and Where in Images Through a Joint Embedding Spacecited by 11
  28. 2022Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Marketcited by 9
  29. 2023Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Predictioncited by 9
  30. 2024Predictive Modeling with Temporal Graphical Representation on Electronic Health Recordscited by 9
  31. 2022Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomographycited by 8
  32. 2022Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Informationcited by 8
  33. 2022LUNA: Language Understanding with Number Augmentations on Transformers via Number Plugins and Pre-trainingcited by 8
  34. 2023Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jetscited by 8
  35. 2024Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encodingcited by 8
  36. 2024RED: Effective Trajectory Representation Learning with Comprehensive Informationcited by 8
  37. 2023Non-Autoregressive Diffusion-based Temporal Point Processes for Continuous-Time Long-Term Event Predictioncited by 7
  38. 2023Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecastingcited by 7

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