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LaT-PFN: A Joint Embedding Predictive Architecture for In-context Time-series Forecasting
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We introduce LatentTimePFN (LaT-PFN), a foundational Time Series model with a strong embedding space that enables zero-shot forecasting. To achieve this, we perform in-context learning in latent space utilizing a novel integration of the Prior-data Fitted Networks (PFN) and Joint Embedding Predictive Architecture (JEPA) frameworks. We leverage the JEPA framework to create a prediction-optimized latent representation of the underlying stochastic process that generates time series and combines it with contextual learning, using a PFN. Furthermore, we improve on preceding works by utilizing related time series as a context and introducing a normalized abstract time axis. This reduces training time and increases the versatility of the model by allowing any time granularity and forecast horizon. We show that this results in superior zero-shot predictions compared to established baselines. We also demonstrate our latent space produces informative embeddings of both individual time steps and fixed-length summaries of entire series. Finally, we observe the emergence of multi-step patch embeddings without explicit training, suggesting the model actively learns discrete tokens that encode local structures in the data, analogous to vision transformers.
Forward citations
Cited by 5 Pith papers
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Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning
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Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
MDA couples an LLM structure proposer with SMC-based Bayesian inference and value-of-information experiment design to identify mechanistic world models with fewer interventions than pure LLM agents.
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Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
A Prior-data Fitted Network with a scaling-law-specific prior gives better point and uncertainty predictions for neural scaling law extrapolation than MCMC, BNSL, and LC-PFN baselines.
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TimeCapsule: Solving the Jigsaw Puzzle of Long-Term Time Series Forecasting with Compressed Predictive Representations
TimeCapsule compresses multivariate time series into a small 3D tensor using learned mode products, forecasts inside that compressed space, and reports state-of-the-art results on ten LTSF benchmarks.
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Position: The Future of Bayesian Prediction Is Prior-Fitted
PFNs, which amortize Bayesian inference by training on datasets sampled from a prior, are likely to supersede MCMC and variational inference for most prediction tasks, the authors argue.
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