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Self-supervised learning for time series analysis: Taxonomy, progress, and prospects
Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
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 …
A survey on spatio-temporal series prediction with deep learning: taxonomy, applications, and future directions
F Sun, W Hao, A Zou, Q Shen - Neural Computing and Applications, 2024 - Springer
With the rapid development of data acquisition and storage technology, spatio-temporal (ST)
data in various fields are growing explosively, so many ST prediction methods have …
data in various fields are growing explosively, so many ST prediction methods have …
Forecasting movements of stock time series based on hidden state guided deep learning approach
Stock movement forecasting is usually formalized as a sequence prediction task based on
time series data. Recently, more and more deep learning models are used to fit the dynamic …
time series data. Recently, more and more deep learning models are used to fit the dynamic …
Filternet: Harnessing frequency filters for time series forecasting
Given the ubiquitous presence of time series data across various domains, precise
forecasting of time series holds significant importance and finds widespread real-world …
forecasting of time series holds significant importance and finds widespread real-world …
U-mixer: An unet-mixer architecture with stationarity correction for time series forecasting
Time series forecasting is a crucial task in various domains. Caused by factors such as
trends, seasonality, or irregular fluctuations, time series often exhibits non-stationary. It …
trends, seasonality, or irregular fluctuations, time series often exhibits non-stationary. It …
Deep coupling network for multivariate time series forecasting
Multivariate time series (MTS) forecasting is crucial in many real-world applications. To
achieve accurate MTS forecasting, it is essential to simultaneously consider both intra-and …
achieve accurate MTS forecasting, it is essential to simultaneously consider both intra-and …
MR-transformer: multiresolution transformer for multivariate time series prediction
Multivariate time series (MTS) prediction has been studied broadly, which is widely applied
in real-world applications. Recently, transformer-based methods have shown the potential in …
in real-world applications. Recently, transformer-based methods have shown the potential in …
Multi-scale attention flow for probabilistic time series forecasting
The probability prediction of multivariate time series is a notoriously challenging but practical
task. On the one hand, the challenge is how to effectively capture the cross-series …
task. On the one hand, the challenge is how to effectively capture the cross-series …