Home > Research > Publications & Outputs > Advances in forecasting with artificial neural ...

Electronic data

View graph of relations

Advances in forecasting with artificial neural networks

Research output: Working paper

Published

Standard

Advances in forecasting with artificial neural networks. / Kourentzes, N; Crone, S.
Lancaster University: The Department of Management Science, 2010. (Management Science Working Paper Series).

Research output: Working paper

Harvard

Kourentzes, N & Crone, S 2010 'Advances in forecasting with artificial neural networks' Management Science Working Paper Series, The Department of Management Science, Lancaster University.

APA

Kourentzes, N., & Crone, S. (2010). Advances in forecasting with artificial neural networks. (Management Science Working Paper Series). The Department of Management Science.

Vancouver

Kourentzes N, Crone S. Advances in forecasting with artificial neural networks. Lancaster University: The Department of Management Science. 2010. (Management Science Working Paper Series).

Author

Kourentzes, N ; Crone, S. / Advances in forecasting with artificial neural networks. Lancaster University : The Department of Management Science, 2010. (Management Science Working Paper Series).

Bibtex

@techreport{b07982e3f2c843b5a326ca56027db9a2,
title = "Advances in forecasting with artificial neural networks",
abstract = "There is decades long research interest in artificial neural networks (ANNs) that has led to several successful applications. In forecasting, both in theoretical and empirical works, ANNs have shown evidence of good performance, in many cases outperforming established benchmark models. However, our understanding of their inner workings is still limited, which makes it difficult for academicians and practitioners alike to use them. Furthermore, while there is a growing literature supporting their good performance in forecasting, there is also a lot of scepticism whether ANNs are able to provide reliable and robust forecasts. This analysis presents the advances of ANNs in the time series forecasting field, highlighting the current state of the art, which modelling issues have been solved and which are still critical for forecasting with ANNs, indicating future research directions.",
keywords = "neural networks, forecasting",
author = "N Kourentzes and S Crone",
year = "2010",
language = "English",
series = "Management Science Working Paper Series",
publisher = "The Department of Management Science",
type = "WorkingPaper",
institution = "The Department of Management Science",

}

RIS

TY - UNPB

T1 - Advances in forecasting with artificial neural networks

AU - Kourentzes, N

AU - Crone, S

PY - 2010

Y1 - 2010

N2 - There is decades long research interest in artificial neural networks (ANNs) that has led to several successful applications. In forecasting, both in theoretical and empirical works, ANNs have shown evidence of good performance, in many cases outperforming established benchmark models. However, our understanding of their inner workings is still limited, which makes it difficult for academicians and practitioners alike to use them. Furthermore, while there is a growing literature supporting their good performance in forecasting, there is also a lot of scepticism whether ANNs are able to provide reliable and robust forecasts. This analysis presents the advances of ANNs in the time series forecasting field, highlighting the current state of the art, which modelling issues have been solved and which are still critical for forecasting with ANNs, indicating future research directions.

AB - There is decades long research interest in artificial neural networks (ANNs) that has led to several successful applications. In forecasting, both in theoretical and empirical works, ANNs have shown evidence of good performance, in many cases outperforming established benchmark models. However, our understanding of their inner workings is still limited, which makes it difficult for academicians and practitioners alike to use them. Furthermore, while there is a growing literature supporting their good performance in forecasting, there is also a lot of scepticism whether ANNs are able to provide reliable and robust forecasts. This analysis presents the advances of ANNs in the time series forecasting field, highlighting the current state of the art, which modelling issues have been solved and which are still critical for forecasting with ANNs, indicating future research directions.

KW - neural networks

KW - forecasting

M3 - Working paper

T3 - Management Science Working Paper Series

BT - Advances in forecasting with artificial neural networks

PB - The Department of Management Science

CY - Lancaster University

ER -