Long input windows are required to identify the generative process in time series forecasting even for short-memory processes, and decoupling identification from forecasting improves scalability.
Zero-shot time series forecasting with covariates via in-context learning,
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 3years
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UNVERDICTED 3representative citing papers
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.
CITRAS-FM is a 7M-param decoder-only Transformer TSFM with Shifted Attention and CovSynth synthetic covariate pretraining that claims SOTA zero-shot accuracy among sub-10M models on fev-bench with sub-0.1s CPU inference.
citing papers explorer
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Why Do Time Series Models Need Long Context Windows?
Long input windows are required to identify the generative process in time series forecasting even for short-memory processes, and decoupling identification from forecasting improves scalability.
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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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CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting
CITRAS-FM is a 7M-param decoder-only Transformer TSFM with Shifted Attention and CovSynth synthetic covariate pretraining that claims SOTA zero-shot accuracy among sub-10M models on fev-bench with sub-0.1s CPU inference.