Home > Research > Publications & Outputs > Modelling Deterministic Seasonality with Artifi...

Electronic data

View graph of relations

Modelling Deterministic Seasonality with Artificial Neural Networks for Time Series Forecasting

Research output: Working paper

Published

Standard

Modelling Deterministic Seasonality with Artificial Neural Networks for Time Series Forecasting. / 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 'Modelling Deterministic Seasonality with Artificial Neural Networks for Time Series Forecasting' Management Science Working Paper Series, The Department of Management Science, Lancaster University.

APA

Kourentzes, N., & Crone, S. (2010). Modelling Deterministic Seasonality with Artificial Neural Networks for Time Series Forecasting. (Management Science Working Paper Series). The Department of Management Science.

Vancouver

Kourentzes N, Crone S. Modelling Deterministic Seasonality with Artificial Neural Networks for Time Series Forecasting. Lancaster University: The Department of Management Science. 2010. (Management Science Working Paper Series).

Author

Kourentzes, N ; Crone, S. / Modelling Deterministic Seasonality with Artificial Neural Networks for Time Series Forecasting. Lancaster University : The Department of Management Science, 2010. (Management Science Working Paper Series).

Bibtex

@techreport{9a17bfe4a8a4465ea8d39227201c4b81,
title = "Modelling Deterministic Seasonality with Artificial Neural Networks for Time Series Forecasting",
abstract = "This study explores both from a theoretical and empirical perspective how to model deterministic seasonality with artificial neural networks (ANN) to achieve the best forecasting accuracy. The aim of this study is to maximise the available seasonal information to the ANN while identifying the most economic form to code it; hence reducing the modelling degrees of freedom and simplifying the network{\textquoteright}s training. An empirical evaluation on simulated and real data is performed and in agreement with the theoretical analysis no deseasonalising is required. A parsimonious coding based on seasonal indices is proposed that showed the best forecasting accuracy.",
keywords = "neural networks, deterministic seasonality, input variable selection",
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 - Modelling Deterministic Seasonality with Artificial Neural Networks for Time Series Forecasting

AU - Kourentzes, N

AU - Crone, S

PY - 2010

Y1 - 2010

N2 - This study explores both from a theoretical and empirical perspective how to model deterministic seasonality with artificial neural networks (ANN) to achieve the best forecasting accuracy. The aim of this study is to maximise the available seasonal information to the ANN while identifying the most economic form to code it; hence reducing the modelling degrees of freedom and simplifying the network’s training. An empirical evaluation on simulated and real data is performed and in agreement with the theoretical analysis no deseasonalising is required. A parsimonious coding based on seasonal indices is proposed that showed the best forecasting accuracy.

AB - This study explores both from a theoretical and empirical perspective how to model deterministic seasonality with artificial neural networks (ANN) to achieve the best forecasting accuracy. The aim of this study is to maximise the available seasonal information to the ANN while identifying the most economic form to code it; hence reducing the modelling degrees of freedom and simplifying the network’s training. An empirical evaluation on simulated and real data is performed and in agreement with the theoretical analysis no deseasonalising is required. A parsimonious coding based on seasonal indices is proposed that showed the best forecasting accuracy.

KW - neural networks

KW - deterministic seasonality

KW - input variable selection

M3 - Working paper

T3 - Management Science Working Paper Series

BT - Modelling Deterministic Seasonality with Artificial Neural Networks for Time Series Forecasting

PB - The Department of Management Science

CY - Lancaster University

ER -