# Joint Embeddings Go Temporal

arXiv: https://arxiv.org/abs/2509.25449 · HTML: https://mlsanity.com/paper/2509.25449/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [MOMENT: A Family of Open Time-series Foundation Models](https://mlsanity.com/paper/2402.03885/) (2024)
- [TS2Vec: Towards Universal Representation of Time Series](https://mlsanity.com/paper/2106.10466/) (2021)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency](https://mlsanity.com/paper/2206.08496/) (2022)
- [A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond](https://mlsanity.com/paper/2208.00173/) (2022)
- [Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks](https://mlsanity.com/paper/2406.04853/) (2024)
- [LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting](https://mlsanity.com/paper/2405.10093/) (2024)

## Built on by (most cited first, 14 of 14)

- [SC-JEPA: Stabilizing Latent Predictive Learning for Time-Series Anomaly Prediction](https://mlsanity.com/paper/2602.04643/) (2026)
- [HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series](https://mlsanity.com/paper/2605.11130/) (2026)
- [EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models](https://mlsanity.com/paper/2602.14024/) (2026)
- [SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting](https://mlsanity.com/paper/2605.00126/) (2026)
- [Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA](https://mlsanity.com/paper/2606.28446/) (2026)
- [Distributed JEPA: A Self-Supervised Framework for Energy Forecasting](https://mlsanity.com/paper/2609.17029/) (2026)
- [Does Joint-Embedding Predictive Architecture Pretraining Help Time Series Forecasting?](https://mlsanity.com/paper/2609.31680/) (2026)
- [What Do Latent Predictive Vehicle Representations Retain? Measuring State, Geometry, and Local Response](https://mlsanity.com/paper/2609.32512/) (2026)
- [Phenomenon-Graph JEPA: Label-Efficient Representation Learning for Contactless Cardiorespiratory Sensing](https://mlsanity.com/paper/2609.32928/) (2026)
- [Repeated-Measure Leakage, Distribution Shift, and Reliability under Partial Observation in Patient World Models](https://mlsanity.com/paper/2610.04778/) (2026)
- [Pythia: Toward Foundation World Models for Multimodal Time Series](https://mlsanity.com/paper/2610.05240/) (2026)
- [BeliefGraph-JEPA: Structured Latent World Models for Action-Conditioned Time Series](https://mlsanity.com/paper/2610.05409/) (2026)
- [STOCK-JEPA: Prior-Anchored Latent Revision Representation Learning in Equity Markets](https://mlsanity.com/paper/2610.07006/) (2026)
- [W-JEPA: Wavelet Joint-Embedding Predictive Architecture for Wearable Activity Recognition](https://mlsanity.com/paper/2610.19411/) (2026)

## Abstract (arXiv)

Self-supervised learning has seen great success recently in unsupervised representation learning, enabling breakthroughs in natural language and image processing. However, these methods often rely on autoregressive and masked modeling, which aim to reproduce masked information in the input, which can be vulnerable to the presence of noise or confounding variables. To address this problem, Joint-Embedding Predictive Architectures (JEPA) has been introduced with the aim to perform self-supervised learning in the latent space. To leverage these advancements in the domain of time series, we introduce Time Series JEPA (TS-JEPA), an architecture specifically adapted for time series representation learning. We validate TS-JEPA on both classification and forecasting, showing that it can match or surpass current state-of-the-art baselines on different standard datasets. Notably, our approach demonstrates a strong performance balance across diverse tasks, indicating its potential as a robust foundation for learning general representations. Thus, this work lays the groundwork for developing future time series foundation models based on Joint Embedding.