Attention-Based Models for Multivariate Time Series Forecasting: Multi-step Solar Irradiation Prediction [PDF]
HeliyonBangladesh's subtropical climate with an abundance of sunlight throughout the greater portion of the year results in increased effectiveness of solar panels.
Sadman Sakib +7 more
doaj +3 more sources
Sparse transformer with local and seasonal adaptation for multivariate time series forecasting [PDF]
Scientific ReportsTransformers have achieved remarkable performance in multivariate time series(MTS) forecasting due to their capability to capture long-term dependencies.
Yifan Zhang +3 more
doaj +2 more sources
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [PDF]
Knowledge Discovery and Data Mining, 2020 Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend
Zonghan Wu +5 more
semanticscholar +1 more source
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting [PDF]
International Conference on Information and Knowledge Management, 2022 Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods due to their state-of-the-art performance ...
Zezhi Shao +4 more
semanticscholar +1 more source
Multivariate Count Data Models for Time Series Forecasting
Entropy, 2021 Count data appears in many research fields and exhibits certain features that make modeling difficult. Most popular approaches to modeling count data can be classified into observation and parameter-driven models. In this paper, we review two models from
Yuliya Shapovalova +2 more
doaj +1 more source
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting [PDF]
Knowledge Discovery and Data Mining, 2022 Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods.
Zezhi Shao +3 more
semanticscholar +1 more source
Skip-RCNN: A Cost-Effective Multivariate Time Series Forecasting Model
IEEE Access, 2023 Multivariate time series (MTS) forecasting is a crucial aspect in many classification and regression tasks. In recent years, deep learning models have become the mainstream framework for MTS forecasting. Among these deep learning methods, the transformer
Haitao Song +6 more
doaj +1 more source
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting [PDF]
Knowledge Discovery and Data Mining, 2023 Transformers have gained popularity in time series forecasting for their ability to capture long-sequence interactions. However, their memory and compute-intensive requirements pose a critical bottleneck for long-term forecasting, despite numerous ...
Vijayabharathi Ekambaram +4 more
semanticscholar +1 more source
Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures [PDF]
arXiv.org, 2022 Multivariate time series forecasting has seen widely ranging applications in various domains, including finance, traffic, energy, and healthcare. To capture the sophisticated temporal patterns, plenty of research studies designed complex neural network ...
T. Zhang +6 more
semanticscholar +1 more source
Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting
IEEE Access, 2023 Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencies and intricate interactions–both direct and indirect.
Juhyeon Kim +5 more
doaj +1 more source