arXiv AI
Jun 10
CITRAS: Covariate-Informed Transformer for Time Series Forecasting
arXiv:2503. 24007v4 Announce Type: replace-cross Abstract: In time series forecasting, covariates represent external factors that influence target variables.
CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting
arXiv:2606. 10798v1 Announce Type: new Abstract: Pretrained time series foundation models (TSFMs) have enabled zero-shot forecasting on unseen target series.
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arXiv Machine Learning
Jun 2
Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting
arXiv:2606. 01289v1 Announce Type: new Abstract: Zero-shot time series forecasting aims to predict future values for previously unseen series, requiring models to generalize temporal dynamics beyond the training distribution.
arXiv Machine Learning
Jul 2
TiRex-2: Generalizing TiRex to Multivariate Data and Streaming
arXiv:2607. 01204v1 Announce Type: new Abstract: We introduce TiRex-2, a recurrent xLSTM-based time series foundation model that generalizes the univariate TiRex to multivariate forecasting with both past and future covariates.
arXiv Machine Learning
Jun 30
fev-bench: A Realistic Benchmark for Time Series Forecasting
arXiv:2509. 26468v3 Announce Type: replace Abstract: Benchmark quality is critical for meaningful evaluation and sustained progress in time series forecasting, particularly with the rise of pretrained models.
arXiv AI
Jul 28
Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting
arXiv:2602. 17634v2 Announce Type: replace-cross Abstract: Learning time series foundation models has been shown to be a promising approach for zero-shot time series forecasting across diverse time series domains.
arXiv AI
Jul 8
RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models
arXiv:2607. 06504v1 Announce Type: new Abstract: Recent years have witnessed the emergence of multivariate modeling using time series foundation models (TSFMs), which achieve advanced zero-shot generalization.
arXiv AI
5d ago
Into the ORBIT for Time Series: Training Regimes for Foundation Models
arXiv:2608. 13262v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) have advanced primarily through architectural innovation, while training regimes for large-scale heterogeneous corpora remain under-explored.
Hugging Face Trending Papers
Jul 7
RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models
Recent years have witnessed the emergence of multivariate modeling using time series foundation models (TSFMs), which achieve advanced zero-shot generalization. Modern multivariate TSFMs are predominantly pretrained on multivariate synthetic data, which is easier to scale but may fail to capture the complex temporal dynamics and cross-variable relationships present in real-world time series.
arXiv AI
Jul 21
FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting
arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.
arXiv Machine Learning
Jun 29
Unified Zero-Shot Time Series Forecasting: A Darts Foundation
arXiv:2606. 27438v1 Announce Type: new Abstract: Since its initial release in 2020, Darts has become a widely used open-source Python library for time series analysis.
arXiv AI
2d ago
PEFT-MuTS: A Multivariate Parameter-Efficient Fine-Tuning Framework for Remaining Useful Life Prediction based on Cross-domain Time Series Representation Model
arXiv:2601. 22631v2 Announce Type: replace-cross Abstract: The application of data-driven remaining useful life (RUL) prediction has long been constrained by the availability of large amount of degradation data.
arXiv AI
Jun 10
Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling
arXiv:2605. 27286v2 Announce Type: replace-cross Abstract: Time series foundation models (TSFMs) are transforming the forecasting paradigm through large-scale cross-domain pretraining.