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 …
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 …
Learning latent seasonal-trend representations for time series forecasting
Forecasting complex time series is ubiquitous and vital in a range of applications but
challenging. Recent advances endeavor to achieve progress by incorporating various deep …
challenging. Recent advances endeavor to achieve progress by incorporating various deep …
A survey on deep learning based time series analysis with frequency transformation
Recently, frequency transformation (FT) has been increasingly incorporated into deep
learning models to significantly enhance state-of-the-art accuracy and efficiency in time …
learning models to significantly enhance state-of-the-art accuracy and efficiency in time …
Dish-ts: a general paradigm for alleviating distribution shift in time series forecasting
The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes
over time, largely hinders the performance of TSF models. Existing works towards …
over time, largely hinders the performance of TSF models. Existing works towards …
Basisformer: Attention-based time series forecasting with learnable and interpretable basis
Bases have become an integral part of modern deep learning-based models for time series
forecasting due to their ability to act as feature extractors or future references. To be …
forecasting due to their ability to act as feature extractors or future references. To be …
Non-autoregressive conditional diffusion models for time series prediction
Recently, denoising diffusion models have led to significant breakthroughs in the generation
of images, audio and text. However, it is still an open question on how to adapt their strong …
of images, audio and text. However, it is still an open question on how to adapt their strong …
Segrnn: Segment recurrent neural network for long-term time series forecasting
RNN-based methods have faced challenges in the Long-term Time Series Forecasting
(LTSF) domain when dealing with excessively long look-back windows and forecast …
(LTSF) domain when dealing with excessively long look-back windows and forecast …
Msgnet: Learning multi-scale inter-series correlations for multivariate time series forecasting
Multivariate time series forecasting poses an ongoing challenge across various disciplines.
Time series data often exhibit diverse intra-series and inter-series correlations, contributing …
Time series data often exhibit diverse intra-series and inter-series correlations, contributing …