Dsformer: A double sampling transformer for multivariate time series long-term prediction
Multivariate time series long-term prediction, which aims to predict the change of data in a
long time, can provide references for decision-making. Although transformer-based models …
long time, can provide references for decision-making. Although transformer-based models …
Revisiting long-term time series forecasting: An investigation on linear map**
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 …
Mixformer: Mixture transformer with hierarchical context for spatio-temporal wind speed forecasting
Wind energy has attracted more and more attention due to its sustainability and pollution-
free nature. As wind energy is highly dependent on wind speed, wind speed forecasting is of …
free nature. As wind energy is highly dependent on wind speed, wind speed forecasting is of …
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 …
[HTML][HTML] SwipeFormer: Transformers for mobile touchscreen biometrics
The growing number of mobile devices over the past few years brings a large amount of
personal information, which needs to be properly protected. As a result, several mobile …
personal information, which needs to be properly protected. As a result, several mobile …
Deep learning-based time series forecasting
With the advancement of deep learning algorithms and the growing availability of
computational power, deep learning-based forecasting methods have gained significant …
computational power, deep learning-based forecasting methods have gained significant …
Frnet: Frequency-based rotation network for long-term time series forecasting
Long-term time series forecasting (LTSF) aims to predict future values for a long time based
on historical data. The period term is an essential component of the time series, which is …
on historical data. The period term is an essential component of the time series, which is …
TCDformer: A transformer framework for non-stationary time series forecasting based on trend and change-point detection
J Wan, N **a, Y Yin, X Pan, J Hu, J Yi - Neural Networks, 2024 - Elsevier
Although time series prediction models based on Transformer architecture have achieved
significant advances, concerns have arisen regarding their performance with non-stationary …
significant advances, concerns have arisen regarding their performance with non-stationary …
High-precision energy consumption forecasting for large office building using a signal decomposition-based deep learning approach
Accurate long-term energy consumption forecasting is crucial for efficient energy
management in large office buildings. Recent research highlights that deep learning …
management in large office buildings. Recent research highlights that deep learning …
DLPformer: A hybrid mathematical model for state of charge prediction in electric vehicles using machine learning approaches
Accurate mathematical modeling of state of charge (SOC) prediction is essential for battery
management systems (BMSs) to improve battery utilization efficiency and ensure a good …
management systems (BMSs) to improve battery utilization efficiency and ensure a good …