Results 31 to 40 of about 223,849 (180)

A Neural Networks Based Method for Multivariate Time-Series Forecasting

IEEE Access, 2021
In recent years, more and more deep neural network methods have been used in the forecasting research of multivariate time series. Comparing to the traditional methods such as autoregressive models, methods based on neural networks have achieved superior
Shaowei Li, He Huang, Wei Lu
doaj   +1 more source

Forecasting Video QoE With Deep Learning From Multivariate Time-Series

IEEE Open Journal of Signal Processing, 2021
The end users’ satisfactory Quality of Experience (QoE) is a fundamental criterion for networked video service providers such as video-on-demand providers (Netflix, YouTube, etc.), cloud gaming providers (Google Stadia, PlayStation Now, etc.) and ...
Hossein Ebrahimi Dinaki, Shervin Shirmohammadi, Emil Janulewicz, David Cote   +3 more
doaj   +1 more source

Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS) [PDF]

, 2019
Universiti Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian Technical University. There is a great development of UTHM since its formation in 1993.
K.G., Tay, Muwafaq, Hassan, W. K., Tiong, Y. Y., Choy   +3 more
core   +1 more source

Multivariate Time Series Forecasting Based on Multi-Scale Feature Fusion and Dual-Attention Mechanism [PDF]

Jisuanji gongcheng, 2023
Each subsequence of the Multivariate Time Series(MTS) contains multi-scale characteristics of different time spans, comprising information such as development process, direction, and trend.
Lu HAN, Weigang HUO, Yonghui ZHANG, Tao LIU
doaj   +1 more source

TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment [PDF]

AAAI Conference on Artificial Intelligence
Multivariate time series forecasting (MTSF) aims to learn temporal dynamics among variables to forecast future time series. Existing statistical and deep learning-based methods suffer from limited learnable parameters and small-scale training data ...
Chenxi Liu, Qianxiong Xu, Hao Miao, Sun Yang, Lingzheng Zhang, Cheng Long, Ziyue Li, Rui Zhao   +7 more
semanticscholar   +1 more source

Multivariate time series prediction by RNN architectures for energy consumption forecasting

Energy Reports, 2022
Households and buildings have been utilizing the traditional electric network structure for the last decade, relying on energy supplied by manufacturing centers based on fossil fuels. Large energy use places a burden on such centers. In this perspective,
Ibtissam Amalou, Naoual Mouhni, Abdelmounaim Abdali   +2 more
doaj   +1 more source

FilterTS: Comprehensive Frequency Filtering for Multivariate Time Series Forecasting [PDF]

AAAI Conference on Artificial Intelligence
Multivariate time series forecasting is crucial across various industries, where accurate extraction of complex periodic and trend components can significantly enhance prediction performance.
Yulong Wang, Yushuo Liu, Xiaoyi Duan, Kai Wang   +3 more
semanticscholar   +1 more source

Post Constraint and Correction: A Plug-and-Play Module for Boosting the Performance of Deep Learning Based Weather Multivariate Time Series Forecasting

Applied Sciences
Weather forecasting is essential for various applications such as agriculture and transportation, and relies heavily on meteorological sequential data such as multivariate time series collected from weather stations.
Zhengrui Wang, Zhongwen Luo, Zirui Yang, Yuanyuan Liu   +3 more
doaj   +1 more source

ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs

Machine Learning and Knowledge Extraction
Large language models (LLMs) have recently demonstrated notable performance, particularly in addressing the challenge of extensive data requirements when training traditional forecasting models.
Jinglei Pei, Yang Zhang, Ting Liu, Jingbin Yang, Qinghua Wu, Kang Qin   +5 more
doaj   +1 more source

Volatility forecasting [PDF]

, 2005
Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical ...
Andersen, Torben G., Bollerslev, Tim, Christoffersen, Peter F., Diebold, Francis X.   +3 more
core   +6 more sources