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Temporal pattern attention for multivariate time series forecasting [PDF]

Machine Learning, 2019
Forecasting multivariate time series data, such as prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate the task. To obtain accurate prediction, it is crucial to model long-term dependency
Shun-Yao Shih, Fan-Keng Sun, Hung-Yi Lee
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Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network

Applied Sciences, 2022
Multivariate time series forecasting has long been a subject of great concern. For example, there are many valuable applications in forecasting electricity consumption, solar power generation, traffic congestion, finance, and so on.
Zichao He, Chunna Zhao, Yaqun Huang
doaj   +1 more source

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

Multi-step CNN forecasting for COVID-19 multivariate time-series

IJAIN (International Journal of Advances in Intelligent Informatics), 2023
The new coronavirus (COVID-19) has spread to over 200 countries, with over 36 million confirmed cases as of October 10, 2020. As a result, numerous machine learning models capable of forecasting the epidemic worldwide have been produced.
Haviluddin Haviluddin, Rayner Alfred
doaj   +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

Multivariate Dynamic Kernels for Financial Time Series Forecasting [PDF]

, 2016
We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process redefines the original financial time series into temporal data blocks, analyzing the temporal information of multiple
Peña Grass, Mauricio, Arratia Quesada, Argimiro Alejandro, Belanche Muñoz, Luis Antonio   +2 more
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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
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Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting

CoRR, 2020
Time series forecasting is an important yet challenging task. Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to improve the interpretability of time series models. Previous interpretation methods, including the methods for general neural networks and attention-based methods, mainly consider
Qingyi Pan, Wenbo Hu 0001, Jun Zhu 0001
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