Deep time series models: A comprehensive survey and benchmark

Y Wang, H Wu, J Dong, Y Liu, M Long… - arxiv preprint arxiv …, 2024 - arxiv.org
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 …

Frequency-domain MLPs are more effective learners in time series forecasting

K Yi, Q Zhang, W Fan, S Wang… - Advances in …, 2024 - proceedings.neurips.cc
Time series forecasting has played the key role in different industrial, including finance,
traffic, energy, and healthcare domains. While existing literatures have designed many …

FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective

K Yi, Q Zhang, W Fan, H He, L Hu… - Advances in …, 2024 - proceedings.neurips.cc
Multivariate time series (MTS) forecasting has shown great importance in numerous
industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods …

Learning latent seasonal-trend representations for time series forecasting

Z Wang, X Xu, W Zhang, G Trajcevski… - Advances in …, 2022 - proceedings.neurips.cc
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 …

A survey on deep learning based time series analysis with frequency transformation

K Yi, Q Zhang, L Cao, S Wang, G Long, L Hu… - arxiv preprint arxiv …, 2023 - arxiv.org
Recently, frequency transformation (FT) has been increasingly incorporated into deep
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

W Fan, P Wang, D Wang, D Wang, Y Zhou… - Proceedings of the AAAI …, 2023 - ojs.aaai.org
The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes
over time, largely hinders the performance of TSF models. Existing works towards …

Basisformer: Attention-based time series forecasting with learnable and interpretable basis

Z Ni, H Yu, S Liu, J Li, W Lin - Advances in Neural …, 2024 - proceedings.neurips.cc
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 …

Non-autoregressive conditional diffusion models for time series prediction

L Shen, J Kwok - International Conference on Machine …, 2023 - proceedings.mlr.press
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 …

Segrnn: Segment recurrent neural network for long-term time series forecasting

S Lin, W Lin, W Wu, F Zhao, R Mo, H Zhang - arxiv preprint arxiv …, 2023 - arxiv.org
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 …

Msgnet: Learning multi-scale inter-series correlations for multivariate time series forecasting

W Cai, Y Liang, X Liu, J Feng, Y Wu - Proceedings of the AAAI …, 2024 - ojs.aaai.org
Multivariate time series forecasting poses an ongoing challenge across various disciplines.
Time series data often exhibit diverse intra-series and inter-series correlations, contributing …