Zobrazeno 1 - 10
of 3 480
pro vyhledávání: '"multivariate time series forecasting"'
Autor:
Liu, Zhiding, Yang, Jiqian, Mao, Qingyang, Zhao, Yuze, Cheng, Mingyue, Li, Zhi, Liu, Qi, Chen, Enhong
Multivariate time series forecasting plays a crucial role in various real-world applications. Significant efforts have been made to integrate advanced network architectures and training strategies that enhance the capture of temporal dependencies, th
Externí odkaz:
http://arxiv.org/abs/2410.22981
Attention-based architectures have become ubiquitous in time series forecasting tasks, including spatio-temporal (STF) and long-term time series forecasting (LTSF). Yet, our understanding of the reasons for their effectiveness remains limited. This w
Externí odkaz:
http://arxiv.org/abs/2410.24023
Autor:
Liang, Aobo, Sun, Yan
In recent years, Transformer-based models (Transformers) have achieved significant success in multivariate time series forecasting (MTSF). However, previous works focus on extracting features either from the time domain or the frequency domain, which
Externí odkaz:
http://arxiv.org/abs/2410.22649
Recently, there has been a growing interest in Long-term Time Series Forecasting (LTSF), which involves predicting long-term future values by analyzing a large amount of historical time-series data to identify patterns and trends. There exist signifi
Externí odkaz:
http://arxiv.org/abs/2410.02081
Time series data is prevalent across numerous fields, necessitating the development of robust and accurate forecasting models. Capturing patterns both within and between temporal and multivariate components is crucial for reliable predictions. We int
Externí odkaz:
http://arxiv.org/abs/2410.16928
Multivariate Time Series (MTS) forecasting is a fundamental task with numerous real-world applications, such as transportation, climate, and epidemiology. While a myriad of powerful deep learning models have been developed for this task, few works ha
Externí odkaz:
http://arxiv.org/abs/2410.02195
Long-term Time Series Forecasting (LTSF) is critical for numerous real-world applications, such as electricity consumption planning, financial forecasting, and disease propagation analysis. LTSF requires capturing long-range dependencies between inpu
Externí odkaz:
http://arxiv.org/abs/2410.02070
Autor:
Jiang, Yue, Li, Xiucheng, Chen, Yile, Liu, Shuai, Kong, Weilong, Lentzakis, Antonis F., Cong, Gao
Time series forecasting is essential for our daily activities and precise modeling of the complex correlations and shared patterns among multiple time series is essential for improving forecasting performance. Spatial-Temporal Graph Neural Networks (
Externí odkaz:
http://arxiv.org/abs/2406.12282
The emergence of deep learning models has revolutionized various industries over the last decade, leading to a surge in connected devices and infrastructures. However, these models can be tricked into making incorrect predictions with high confidence
Externí odkaz:
http://arxiv.org/abs/2408.14875
Autor:
Zhou, Xin, Wang, Weiqing, Buntine, Wray, Qu, Shilin, Sriramulu, Abishek, Tan, Weicong, Bergmeir, Christoph
Deep models for Multivariate Time Series (MTS) forecasting have recently demonstrated significant success. Channel-dependent models capture complex dependencies that channel-independent models cannot capture. However, the number of channels in real-w
Externí odkaz:
http://arxiv.org/abs/2408.04245