Dilated convolutional neural networks for time series forecasting

By Anastasia Borovykh, Sander M. Bohte, Cornelis W. Oosterlee. 2018

117 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Signal Processing, Time-Series & Forecasting

How researchers see this paper

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

In Artificial Intelligence

1. Omer Berat Sezer
2. M. Ugur Gudelek
3. Manuel Carranza-García
4. Pedro Lara-Benítez
5. José María Luna-Romera
6. A. Ozbayoglu
7. J. Riquelme

In Management Science and Operations Research

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Manuel Carranza-García
5. Pedro Lara-Benítez
6. José María Luna-Romera
7. J. Riquelme
8. Weiwei Jiang

In Signal Processing

1. A. Ozbayoglu
2. Omer Berat Sezer
3. M. Ugur Gudelek

In Time-Series & Forecasting

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Manuel Carranza-García
5. Pedro Lara-Benítez
6. Weiwei Jiang
7. Longbing Cao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 114] used S&P500 as their dataset.”
• Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting (2020, methodology): “Several works have already successfully used TCNs for time series forecasting tasks: the original architecture using stacked dilated convolutions was proposed in [25] to improve the performance of LSTM networks for financial domain problems; Ref.”