Modelling Deterministic Seasonality with Artificial Neural Networks for Time Series Forecasting

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

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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’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.

Item Type:
Monograph (Working Paper)
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/aacsb/disciplinebasedresearch
Subjects:
ID Code:
49004
Deposited By:
Deposited On:
11 Jul 2011 21:28
Refereed?:
No
Published?:
Published
Last Modified:
26 Nov 2020 08:12