STAIR's three-stage training enables simple temporal models to match or exceed complex baselines on long-term forecasting benchmarks by combining shared learning, individual adaptation, and residual cross-variable modeling.
Long-term forecasting with tide: Time-series dense encoder
4 Pith papers cite this work. Polarity classification is still indexing.
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GeoCert uses hyperbolic geometry to unify forecasting with physical reasoning and built-in formal certification, claiming major gains in accuracy and efficiency.
UniMamba integrates Mamba state-space dynamics with attention layers and transforms like FFT-Laplace to outperform prior models on multivariate time series forecasting benchmarks.
By applying attention and feed-forward networks to inverted variate tokens instead of temporal tokens, iTransformer achieves state-of-the-art performance on real-world time series forecasting datasets.
citing papers explorer
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Three-Stage Learning Unlocks Strong Performance in Simple Models for Long-Term Time Series Forecasting
STAIR's three-stage training enables simple temporal models to match or exceed complex baselines on long-term forecasting benchmarks by combining shared learning, individual adaptation, and residual cross-variable modeling.
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GeoCert: Certified Geometric AI for Reliable Forecasting
GeoCert uses hyperbolic geometry to unify forecasting with physical reasoning and built-in formal certification, claiming major gains in accuracy and efficiency.
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UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration
UniMamba integrates Mamba state-space dynamics with attention layers and transforms like FFT-Laplace to outperform prior models on multivariate time series forecasting benchmarks.
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iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
By applying attention and feed-forward networks to inverted variate tokens instead of temporal tokens, iTransformer achieves state-of-the-art performance on real-world time series forecasting datasets.