Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

By Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Bernie Wang, Danielle C. Maddix, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella and 3 more. ACM Computing Surveys, 2020

391 citations recorded by the source corpus.

Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches. Consequently, over the last years, these methods are now ubiquitous in large-scale industrial forecasting applications and have consistently ranked among the best entries in forecasting competitions (e.g., M4 and M5). This practical success has further increased the academic interest to understand and improve deep forecasting methods. In this article we provide an introduction and overview of the field: We present important building blocks for deep forecasting in some depth; using these building blocks, we then survey the breadth of the recent deep forecasting literature.

Subfields: Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2c2be8306911e0464365d9b4576445d833afddc1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Junchi Yan
2. Ziqing Ma
3. Liang Sun
4. Qingsong Wen
5. Tian Zhou
6. Qiang Xu
7. Ailing Zeng

In Signal Processing

1. Junchi Yan
2. Ziqing Ma
3. Liang Sun
4. Qingsong Wen
5. Tian Zhou
6. Qiang Xu
7. Ailing Zeng

Citations in the corpus

• Transformers in Time Series: A Survey (2022, background): “We note that there exist several surveys related to deep learning for time series, including forecasting [ Lim and Zohren, 2021; Benidis et al. , 2022; Torres et al. , 2021 ] , classification [ Is-mail Fawaz et al. , 2019 ] , anomaly detection [ Choi et al. , 2021; Bl ´ azquez-Garc ´ ıa et al. ,…”
• Is Mamba Effective for Time Series Forecasting? (2024, positive): “For instance, we evaluate whether Mamba demonstrates generalization capabilities comparable to those of the Transformer in handling TSF data.”