Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data

Kajitani, Yoshio; Hipel, Keith W.; McLeod, A. Ian

doi:10.1002/for.940

Cited by 63 publications

(47 citation statements)

References 24 publications

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“…ANN application areas includes data classification and pattern recognition (Ripley, 1996), damage detection and earthquake simulation (Pei et al, 2006), function approximation (Toh, 1999;Ye and Lin, 2003), material science (Bhadeshia, 1999), experimental design of engineering systems (Röpke et al, 2005), nonlinear optimization (Malek et al, 2010), polypeptide structure prediction (Dorn and de Souza, 2010), prediction of trading signals of stock market indices (Tilakaratne et al, 2008), regression analysis (De Veux et al, 1998), signal and image processing (Watkin, 1993;Masters, 1994), time series analysis and forecasting (Franses and van Dijk, 2000;Kajitani et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

Calibrating artificial neural networks by global optimization

Pintér

2012

Expert Systems with Applications

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An artificial neural network (ANN) is a computational model − implemented as a computer program − that is aimed at emulating the key features and operations of biological neural networks. ANNs are extensively used to model unknown or unspecified functional relationships between the input and output of a "black box" system. In order to apply such a generic procedure to actual decision problems, a key requirement is ANN training to minimize the discrepancy between modeled and measured system output. In this work, we consider ANN training as a (potentially) multi-modal optimization problem. To address this issue, we introduce a global optimization (GO) framework and corresponding GO software. The practical viability of the GO based approach is illustrated by finding close numerical approximations of (one-dimensional, but non-trivial) functions.

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Section: Discussionmentioning

confidence: 99%

Calibrating artificial neural networks by global optimization

Pintér

2012

Expert Systems with Applications

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“…This data consists of the set of annual numbers of lynx trappings in the Mackenzie River District of North-West Canada for the period from 1821 to 1934. The Canada lynx data, which is plotted in Figure 1, was also examined by Kajitani et al [16], beyond the other various studies in the time series literature with a focus on the nonlinear modeling [17]. Following other studies, the logarithms (to the base 10) of the data is used in the analysis.…”

Section: Application Of the Hybrid Methodologymentioning

confidence: 99%

A New Hybrid Methodology for Nonlinear Time Series Forecasting

Khashei

Bijari

2011

Modelling and Simulation in Engineering

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Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of forecasting problems with a high degree of accuracy. However, using ANNs to model linear problems have yielded mixed results, and hence; it is not wise to apply them blindly to any type of data. This is the reason that hybrid methodologies combining linear models such as ARIMA and nonlinear models such as ANNs have been proposed in the literature of time series forecasting. Despite of all advantages of the traditional methodologies for combining ARIMA and ANNs, they have some assumptions that will degenerate their performance if the opposite situation occurs. In this paper, a new methodology is proposed in order to combine the ANNs with ARIMA in order to overcome the limitations of traditional hybrid methodologies and yield more general and more accurate hybrid models. Empirical results with Canadian Lynx data set indicate that the proposed methodology can be a more effective way in order to combine linear and nonlinear models together than traditional hybrid methodologies. Therefore, it can be applied as an appropriate alternative methodology for hybridization in time series forecasting field, especially when higher forecasting accuracy is needed.

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“…NN zur Modellierung und Prognose von Zeitreihen wurden in zahlreichen Anwendungsgebieten eingesetzt, z.B. Sonnenfleckenzeitreihen (Weigend et al, 1990;Medeiros et al, 2006), Flugverkehrzeitreihen (Faraway und Chatfield, 1998), betriebswirtschaftliche Zeitreihen (Balkin und Ord, 2000), Zeitreihen zu Fangzahlen von Luchsen (Zhang, 2003;Kajitani et al, 2005), Elektrizitätsverbrauchszeitreihen (Darbellay und Slama, 2000;Hippert et al, 2001Hippert et al, , 2005, Aktien-und Wechselkurse (Weigend et al, 1990;Refenes et al, 1994;Franses und van Griensven, 1997).…”

Section: Neuronale Netze Zur Zeitreihenmodellierung Und -Prognose Imüunclassified

Prognose makroökonomischer Zeitreihen: Ein Vergleich linearer Modelle mit neuronalen Netzen

Köller¹

2014

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This thesis examines the potential of neural networks (NN) for forecasting of macroeconomic time series in comparison with linear models. The emphasis is on the autoregressive neural network (ARNN) model, which can be seen as a generalisation of the conventional autoregressive (AR) model where the non-linear part is implemented by a neural network of the feedforward type.Among the properties of macroeconomic time series that motivate various adaptations and enhancements both of linear and of neural network methodology are the following: high stochasticity, short length, non-stationary and seasonal behaviour and the fact that the existence of non-linearity is not clearly known a priori. The development and evaluation of the methods is based on simulated time series with known non-linear properties and on two macroeconomic time series: Austrian unemployment rate and Austrian industrial production index. Both series are in monthly observations and are seasonally unadjusted.After the introduction, the first main chapter of the thesis summarizes the basic concepts of linear univariate time series analysis and applies the linear methodology to the two selected macroeconomic time series. Particular attention is devoted to the problem of finding the appropriate differencing filter -conventional differences, seasonal differences or both -as a way to induce stationarity. Amongst other methods unit root tests are employed. The used linear models are the autoregressive (AR) and the autoregressive moving average (ARMA) model. These are augmented by a model part for deterministic seasonality. The models estimated on the two time series have rather high model order and, despite using a sparse modelling technique, comprise relatively many coefficients.The second main chapter treats various theoretic aspects and types of nonlinearity and carries out a sequence of hypothesis tests for non-linearity, in order to be able to rate the possible benefits of applying neural network methods to the two selected time series. The results of these tests indicate the presence of non-linear structure of the additive type, which can be exploited for better forecasting with ARNN models. However, these results have to be interpreted with care, as they might bei compromised by non-linearity of the multiplicative type, residual linear structure and possible structural breaks.The third main chapter introduces the ARNN model and develops methods for its estimation and specification. The specification contains a model part of determistic seasonality, includes a linear part and allows for sparse specification of coefficients. The generalisation capability of the ARNN model is to be ensured by four alternative model building approaches: the statistical-parametric approach with hypothesis testing and pruning, the classical approach with early stopping, the approach with regularisation and the Bayesian evidence framework. For each one of these approaches modifications and additional heuristics are contributed that seem appropriate in view of the a...

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Forecasting nonlinear time series with feed-forward neural networks: a case study of Canadian lynx data

Cited by 63 publications

References 24 publications

Calibrating artificial neural networks by global optimization

Calibrating artificial neural networks by global optimization

A New Hybrid Methodology for Nonlinear Time Series Forecasting

Prognose makroökonomischer Zeitreihen: Ein Vergleich linearer Modelle mit neuronalen Netzen

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