TiRex-2 is a recurrent xLSTM time series foundation model for multivariate forecasting with future covariates and constant-cost streaming that reports SOTA zero-shot results on GIFT-Eval and fev-bench.
General time transformer: an encoder-only foundation model for zero-shot multivariate time series forecasting
5 Pith papers cite this work, alongside 12 external citations. Polarity classification is still indexing.
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ReSGA, a large autoencoder, outperforms prior methods on joint VaR-ES forecasting for US equities and converts the edge into economic gains via a size-enhanced momentum strategy, with gains attributed to data complexity.
Zero-shot TSFMs conditioned on leakage-safe covariates from Google Trends and an institutional index forecast commencing enrolments competitively with classical methods under data sparsity.
This is the first comprehensive survey of OOD generalization methodologies for time series, organized across data distribution, representation learning, and OOD evaluation.
Normalization choice significantly influences training convergence and forecasting performance in causal large time-series models.
citing papers explorer
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TiRex-2: Generalizing TiRex to Multivariate Data and Streaming
TiRex-2 is a recurrent xLSTM time series foundation model for multivariate forecasting with future covariates and constant-cost streaming that reports SOTA zero-shot results on GIFT-Eval and fev-bench.
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ReSGA: A Large Tail Risk Model for Learning Value-at-Risk and Expected Shortfall
ReSGA, a large autoencoder, outperforms prior methods on joint VaR-ES forecasting for US equities and converts the edge into economic gains via a size-enhanced momentum strategy, with gains attributed to data complexity.
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Forecasting Commencing Enrolments Under Data Sparsity: A Zero-Shot Time Series Foundation Models Framework for Higher Education Planning
Zero-shot TSFMs conditioned on leakage-safe covariates from Google Trends and an institutional index forecast commencing enrolments competitively with classical methods under data sparsity.
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Out-of-Distribution Generalization in Time Series: A Survey
This is the first comprehensive survey of OOD generalization methodologies for time series, organized across data distribution, representation learning, and OOD evaluation.
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Does Normalization Choice Matter for Causal Large Time-Series Models?
Normalization choice significantly influences training convergence and forecasting performance in causal large time-series models.