Zobrazeno 1 - 10
of 34
pro vyhledávání: '"ZHOU, TIAN"'
Motivated by the abundance of functional data such as time series and images, there has been a growing interest in integrating such data into neural networks and learning maps from function spaces to R (i.e., functionals). In this paper, we study the
Externí odkaz:
http://arxiv.org/abs/2403.12187
Time series analysis is vital for numerous applications, and transformers have become increasingly prominent in this domain. Leading methods customize the transformer architecture from NLP and CV, utilizing a patching technique to convert continuous
Externí odkaz:
http://arxiv.org/abs/2402.05830
Accurate solar power forecasting is crucial to integrate photovoltaic plants into the electric grid, schedule and secure the power grid safety. This problem becomes more demanding for those newly installed solar plants which lack sufficient data. Cur
Externí odkaz:
http://arxiv.org/abs/2402.05823
Time series forecasting is essential for many practical applications, with the adoption of transformer-based models on the rise due to their impressive performance in NLP and CV. Transformers' key feature, the attention mechanism, dynamically fusing
Externí odkaz:
http://arxiv.org/abs/2402.05370
Spatio-temporal forecasting, pivotal in numerous fields, hinges on the delicate equilibrium between isolating nuanced patterns and sifting out noise. To tackle this, we introduce Sparse Regression-based Vector Quantization (SVQ), a novel technique th
Externí odkaz:
http://arxiv.org/abs/2312.03406
Autor:
Zhou, Tian-Yi, Huo, Xiaoming
This paper studies the binary classification of unbounded data from ${\mathbb R}^d$ generated under Gaussian Mixture Models (GMMs) using deep ReLU neural networks. We obtain $\unicode{x2013}$ for the first time $\unicode{x2013}$ non-asymptotic upper
Externí odkaz:
http://arxiv.org/abs/2308.08030
Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundamental challenge for this task is to learn a representation map that ena
Externí odkaz:
http://arxiv.org/abs/2306.10347
Transformer-based models have emerged as promising tools for time series forecasting. However, these model cannot make accurate prediction for long input time series. On the one hand, they failed to capture global dependencies within time series data
Externí odkaz:
http://arxiv.org/abs/2306.08325
Autor:
Liu, Hengbo, Ma, Ziqing, Yang, Linxiao, Zhou, Tian, Xia, Rui, Wang, Yi, Wen, Qingsong, Sun, Liang
Accurate prediction of electric load is crucial in power grid planning and management. In this paper, we solve the electric load forecasting problem under extreme events such as scorching heats. One challenge for accurate forecasting is the lack of t
Externí odkaz:
http://arxiv.org/abs/2306.08299
Recent studies have demonstrated the great power of Transformer models for time series forecasting. One of the key elements that lead to the transformer's success is the channel-independent (CI) strategy to improve the training robustness. However, t
Externí odkaz:
http://arxiv.org/abs/2305.12095