Good Papers

Showing papers from Shandong Technology and Business University Show all papers

57%Worth a look
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NeurIPS 2026LudongShandong Technology and BusinessTime series forecasting

TailFix: Mitigating Error Accumulation and Correcting Tail Deterioration in Long-Horizon Forecasting

Hua Wang, Haijing Gao, Fan Zhang

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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medium 0/10
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57%Worth a look
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NeurIPS 2026LudongShandong Technology and BusinessTime series forecasting

DSR-TSF: Spectrum-Driven Dynamic Routing for Efficient Long-Horizon Time Series Forecasting

Hua Wang, Bing Li, Fan Zhang

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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NeurIPS 2026ludongShandong Technology and BusinessTime series forecasting

FISC: Time-Series Forecasting via First-Layer Statistical Calibration Constraints

Hua Wang, Xiyuan Zhang, Fan Zhang

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
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medium 0/10
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NeurIPS 2026ludongShandong Technology and BusinessTime series forecasting

BucpTSF: Breaking the Uniform Computation Paradigm in Time-Series Forecasting

Hua Wang, Jinghao Lu, Fan Zhang

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
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NeurIPS 2026SpotlightShandong Technology and BusinessTime series forecasting

Volatility-Whitened Probabilistic Residual Modeling for Long-Term Time Series Forecasting

Fan Zhang, Shiming Fan, Zexuan Ma, Shijun Chen and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
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NeurIPS 2026Shandong Technology and BusinessTime series forecasting

QWaveNet: Quantum-Enhanced Wavelet Network for Time Series Forecasting

Fan Zhang, Shijun Chen, Meijia Wang, Shiming Fan and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 0 of 20 reviewers recommend it
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medium 0/10
strict 0/5