홈 › Papers

Deep Time-series Forecasting Needs Kernelized Moment Balancing

2026-01-31 · Licheng Pan, Hao Wang, Haocheng Yang, Yuqi Li, Qingsong Wen, Xiaoxi Li, Zhichao Chen, Haoxuan Li, Zhixuan Chu, Yuan Lu arxiv

Deep time-series forecasting can be formulated as a distribution balancing problem aimed at aligning the distribution of the forecasts and ground truths. According to Imbens' criterion, true distribution balance requires matching the first moments with respect to any balancing function. We demonstrate that existing objectives fail to meet this criterion, as they enforce moment matching only for one or two predefined balancing functions, thus failing to achieve full distribution balance. To address this limitation, we propose direct forecasting with kernelized moment balancing (KMB-DF). Unlike existing objectives, KMB-DF adaptively selects the most informative balancing functions from a reproducing kernel hilbert space (RKHS) to enforce sufficient distribution balancing. We derive a tractable and differentiable objective that enables efficient estimation from empirical samples and seamless integration into gradient-based training pipelines. Extensive experiments across multiple models and datasets show that KMB-DF consistently improves forecasting accuracy and achieves state-of-the-art performance. Code is available at https://anonymous.4open.science/r/KMB-DF-403C.

📄 PDF Abstract BibTeX arXiv:2602.00717

Code (0)

등록된 구현이 없습니다.

QuaCK-TSF: Quantum-Classical Kernelized Time Series Forecasting

2024-08-21 · Abdallah Aaraba, Soumaya Cherkaoui, Ola Ahmad, Jean-Frédéric Laprade 외

Forecasting in probabilistic time series is a complex endeavor that extends beyond predicting future values to also quantifying the uncertainty inherent in these predictions. Gaussian process regression stands out as a B…

Bayesian Optimization Time Series Time Series Forecasting

A Framework for Imbalanced Time-series Forecasting

2021-07-22 · Luis P. Silvestrin, Leonardos Pantiskas, Mark Hoogendoorn

Time-series forecasting plays an important role in many domains. Boosted by the advances in Deep Learning algorithms, it has for instance been used to predict wind power for eolic energy production, stock market fluctuat…

Time Series Time Series Analysis Time Series Forecasting

Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive Learning

2024-01-31 · Haozhi Gao, Qianqian Ren, Jinbao Li

Contrastive representation learning is crucial in time series analysis as it alleviates the issue of data noise and incompleteness as well as sparsity of supervision signal. However, existing constrastive learning framew…

Contrastive Learning Data Augmentation Representation Learning Time Series+2

ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons

2023-10-11 · Jiawen Zhang, Xumeng Wen, Zhenwei Zhang, Shun Zheng 외

Delivering precise point and distributional forecasts across a spectrum of prediction horizons represents a significant and enduring challenge in the application of time-series forecasting within various industries. Prio…

Benchmarking Position Time Series Time Series Forecasting

Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach

2017-10-09 · Kasun Bandara, Christoph Bergmeir, Slawek Smyl

With the advent of Big Data, nowadays in many applications databases containing large quantities of similar time series are available. Forecasting time series in these domains with traditional univariate forecasting proc…

Benchmarking Clustering Time Series Time Series Analysis+3