Extreme Adaptive Transformer for Time Series Forecasting
Time series forecasting remains challenging when the underlying data contain rare but critical extreme events. This issue is particularly important in hydrologic forecasting, where streamflow distributions are often highly skewed and extreme peaks can have substantial impacts on flood monitoring, water resource management, and early warning systems. Although Transformer-based forecasting models have achieved strong performance by modeling long-range temporal dependencies, they typically treat all time points uniformly and may therefore underrepresent rare extreme patterns. In this paper, we pr
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- PossiblePossibly related (embedding) · 47%unit8co/darts →
- PossiblePossibly related (embedding) · 46%How does a 102M-parameter transformer forecast multivariate time series? →
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzySimilar title/name (fuzzy) · 59%sktime/pytorch-forecasting →
“Fuzzy title match (0.73): “Extreme Adaptive Transformer for Time Series Forecasting” ≈ “sktime/pytorch-forecasting””
- FuzzySimilar title/name (fuzzy) · 59%amazon-science/chronos-forecasting →
“Fuzzy title match (0.73): “Extreme Adaptive Transformer for Time Series Forecasting” ≈ “amazon-science/chronos-forecasting””
- LinkedLinked via arxiv author · 85%Sanjeev Shrestha →
“Extreme Adaptive Transformer for Time Series Forecasting”
- LinkedLinked via arxiv author · 85%Hui Liu →
“Extreme Adaptive Transformer for Time Series Forecasting”
- LinkedLinked via arxiv author · 85%Yifan Zhang →
“Extreme Adaptive Transformer for Time Series Forecasting”