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Jianping Zhu

2 accepted papers

2026

Scale-Invariant Conditional VAE for Coarse-Grained Economic Time-Series Forecasting

IJCAI 2026

Coarse-grained time series (CGTS) are critical for business and macroeconomic analysis. However, CGTS are typically updated infrequently and contain few observations, so model-centric training on raw data is prone to overfitting and degraded forecast accuracy. To address this, we propose SI-CVAE, a

Cited by 0Scholar
2024

Adaptive Meta-Learning Probabilistic Inference Framework for Long Sequence Prediction

AAAI 2024technical

Long sequence prediction has broad and significant application value in fields such as finance, wind power, and weather. However, the complex long-term dependencies of long sequence data and the potential domain shift problems limit the effectiveness of traditional models in practical scenarios. To…