[HTML][HTML] Deep Time Series Forecasting Models: A Comprehensive Survey
X Liu, W Wang - Mathematics, 2024 - mdpi.com
Deep learning, a crucial technique for achieving artificial intelligence (AI), has been
successfully applied in many fields. The gradual application of the latest architectures of …
successfully applied in many fields. The gradual application of the latest architectures of …
Deep Time Series Models: A Comprehensive Survey and Benchmark
Time series, characterized by a sequence of data points arranged in a discrete-time order,
are ubiquitous in real-world applications. Different from other modalities, time series present …
are ubiquitous in real-world applications. Different from other modalities, time series present …
Wave predictor models for medium and long term based on dual attention-enhanced Transformer
L Wang, X Wang, C Dong, Y Sun - Ocean Engineering, 2024 - Elsevier
Ocean wave is an important phenomenon under ocean climate conditions, and accurate
prediction of wave parameters is of particularly critical for offshore operations and marine …
prediction of wave parameters is of particularly critical for offshore operations and marine …
CLeaRForecast: Contrastive Learning of High-Purity Representations for Time Series Forecasting
J Gao, Y Hu, Q Cao, S Dai, Y Chen - arXiv preprint arXiv:2312.05758, 2023 - arxiv.org
Time series forecasting (TSF) holds significant importance in modern society, spanning
numerous domains. Previous representation learning-based TSF algorithms typically …
numerous domains. Previous representation learning-based TSF algorithms typically …
MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer
The massive generation of time-series data by large-scale Internet of Things (IoT) devices
necessitates the exploration of more effective models for multivariate time-series forecasting …
necessitates the exploration of more effective models for multivariate time-series forecasting …
U-shaped Transformer: Retain High Frequency Context in Time Series Analysis
Q Chen, Y Zhang - arXiv preprint arXiv:2307.09019, 2023 - arxiv.org
Time series prediction plays a crucial role in various industrial fields. In recent years, neural
networks with a transformer backbone have achieved remarkable success in many domains …
networks with a transformer backbone have achieved remarkable success in many domains …
MILET: multimodal integration and linear enhanced transformer for electricity price forecasting
L Zhao, L Lu, X Yu - Systems Science & Control Engineering, 2024 - Taylor & Francis
The electricity market is a complex and dynamic environment characterized by a multitude of
factors that influence electricity prices. Accurate and reliable electricity price forecasting …
factors that influence electricity prices. Accurate and reliable electricity price forecasting …
DFINet: Dual-way Feature Interaction Network for Long-Term Series Forecasting
G Zhang, A Zhang, B Li, Y Lin, J Jiang… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Intelligent long-term series forecasting is of great importance in the field of Instrumentation &
Measurement (IM), as it provides advanced insights, optimizes decision-making processes …
Measurement (IM), as it provides advanced insights, optimizes decision-making processes …
Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic power forecasting
J Gao, Q Cao, Y Chen, D Zhang - arXiv preprint arXiv:2406.03808, 2024 - arxiv.org
Photovoltaic (PV) power forecasting plays a crucial role in optimizing the operation and
planning of PV systems, thereby enabling efficient energy management and grid integration …
planning of PV systems, thereby enabling efficient energy management and grid integration …
CVTN: Cross Variable and Temporal Integration for Time Series Forecasting
H Zhou, Y Chen - arXiv preprint arXiv:2404.18730, 2024 - arxiv.org
In multivariate time series forecasting, the Transformer architecture encounters two
significant challenges: effectively mining features from historical sequences and avoiding …
significant challenges: effectively mining features from historical sequences and avoiding …