Frequency-domain MLPs are more effective learners in time series forecasting
Time series forecasting has played the key role in different industrial, including finance,
traffic, energy, and healthcare domains. While existing literatures have designed many …
traffic, energy, and healthcare domains. While existing literatures have designed many …
FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective
Multivariate time series (MTS) forecasting has shown great importance in numerous
industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods …
industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods …
Adaptive normalization for non-stationary time series forecasting: A temporal slice perspective
Deep learning models have progressively advanced time series forecasting due to their
powerful capacity in capturing sequence dependence. Nevertheless, it is still challenging to …
powerful capacity in capturing sequence dependence. Nevertheless, it is still challenging to …
Revisiting long-term time series forecasting: An investigation on linear mapping
Long-term time series forecasting has gained significant attention in recent years. While
there are various specialized designs for capturing temporal dependency, previous studies …
there are various specialized designs for capturing temporal dependency, previous studies …
[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 …
Estimating treatment effects from irregular time series observations with hidden confounders
Causal analysis for time series data, in particular estimating individualized treatment effect
(ITE), is a key task in many real world applications, such as finance, retail, healthcare, etc …
(ITE), is a key task in many real world applications, such as finance, retail, healthcare, etc …
Take an irregular route: Enhance the decoder of time-series forecasting transformer
L Shen, Y Wei, Y Wang, H Qiu - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
With the development of Internet of Things (IoT) systems, precise long-term forecasting
method is requisite for decision makers to evaluate current statuses and formulate future …
method is requisite for decision makers to evaluate current statuses and formulate future …
The univariate model for long-term wind speed forecasting based on wavelet soft threshold denoising and improved Autoformer
G Ban, Y Chen, Z Xiong, Y Zhuo, K Huang - Energy, 2024 - Elsevier
Accurate wind speed prediction is crucial for effective wind power grid integration and
energy dispatching. Recent research has explored the combination of decomposition …
energy dispatching. Recent research has explored the combination of decomposition …
Memda: forecasting urban time series with memory-based drift adaptation
Urban time series data forecasting featuring significant contributions to sustainable
development is widely studied as an essential task of the smart city. However, with the …
development is widely studied as an essential task of the smart city. However, with the …
Dewp: Deep expansion learning for wind power forecasting
Wind is one kind of high-efficient, environmentally-friendly, and cost-effective energy source.
Wind power, as one of the largest renewable energy in the world, has been playing a more …
Wind power, as one of the largest renewable energy in the world, has been playing a more …