| Foundation Models for Time Series: A Survey |
2025-04-05 |
none |
A taxonomy-focused survey of transformer-based foundation models for time series, organized by architecture design, prediction type, series type, model scale, and training objective. |
| Out-of-Distribution Generalization in Time Series: A Survey |
2025-03-18 |
none |
A survey of time-series OOD generalization methods structured around data distribution, representation learning, and evaluation, with application scenarios and open challenges. |
| Deep Learning for Time Series Forecasting: A Survey |
2025-03-13 |
none |
A recent review of deep time-series forecasting that summarizes model families, feature extraction methods, datasets, and future research challenges. |
| Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey |
2024-05-04 |
none |
A modality-aware and challenge-oriented survey covering time-series, language, and vision pretraining paradigms for time series tasks, with updated versions through 2025. |
| Foundation Models for Time Series Analysis: A Tutorial and Survey |
2024-03-21 |
none |
A tutorial-style survey that systematizes architectures, pre-training strategies, adaptation methods, and data modalities for time series foundation models. |
| A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection |
2023-07-07 |
code |
A comprehensive review of graph neural networks for time series analysis across forecasting, classification, anomaly detection, and imputation. |
| Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects |
2023-06-17 |
code |
A taxonomy of self-supervised learning methods for time series, plus commonly used datasets and future directions. |
| A Survey on Time-Series Pre-Trained Models |
2023-05-17 |
code |
A review of time-series pre-trained models that organizes the area by pre-training technique and typical deep-learning backbones. |
| Weakly Supervised Anomaly Detection: A Survey |
2023-02-09 |
code |
A survey of weakly supervised anomaly detection methods across tabular, graph, time-series, and image/video data. |
| Deep Learning for Time Series Anomaly Detection: A Survey |
2022-11-10 |
code |
A structured survey of deep-learning-based time-series anomaly detection models and their taxonomy. |
| A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting |
2022-11-05 |
code |
A review of common regression loss functions for time-series forecasting and the settings where they are useful. |
| Transformers in Time Series: A Survey |
2022-02-15 |
code |
A survey of Transformer variants for time-series modeling, with emphasis on design choices, strengths, and limitations. |
| Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines |
2021-09-02 |
none |
A review of deep-learning-based anomaly detection for time series, including benchmark comparisons and model-selection guidance. |
| DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction |
2021-08-20 |
code |
A survey and benchmark for urban traffic prediction models and datasets under a shared evaluation setup. |
| Generative Adversarial Networks in Time Series: A Survey and Taxonomy |
2021-07-23 |
code |
A taxonomy of GAN variants for discrete and continuous time series, including evaluation metrics and privacy discussion. |
| A Unifying Review of Deep and Shallow Anomaly Detection |
2020-09-24 |
none |
A review that connects shallow and deep anomaly-detection methods and highlights practical challenges and research directions. |
| An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks |
2020-07-30 |
code |
An empirical survey of time-series data-augmentation families evaluated across many datasets and network architectures. |
| Time Series Forecasting With Deep Learning: A Survey |
2020-04-28 |
none |
A survey of deep-learning architectures for one-step and multi-horizon forecasting, including hybrid statistical-neural approaches. |
| Deep Learning for Time Series Forecasting: Tutorial and Literature Survey |
2020-04-21 |
none |
An introduction to deep forecasting that explains key building blocks and surveys the early deep-learning literature. |
| Time Series Data Augmentation for Deep Learning: A Survey |
2020-02-28 |
none |
A structured review of time-series data augmentation methods, their taxonomy, and their empirical impact across tasks. |
| A Review on Outlier/Anomaly Detection in Time Series Data |
2020-02-11 |
none |
A review of outlier and anomaly detection techniques for time series, organized around the main characteristics of each approach. |
| Financial Time Series Forecasting with Deep Learning: A Systematic Literature Review: 2005-2019 |
2019-11-29 |
none |
A systematic review of deep-learning methods for financial time-series forecasting across domains and model families. |