Some recent developments in non-linear time series modelling, testing, and forecasting

Gooijer, Jan G. De; Kumar, Kuldeep

doi:10.1016/0169-2070(92)90115-p

Cited by 178 publications

(69 citation statements)

References 34 publications

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“…De Gooijer & Kumar (1992) provided an overview of the developments in this area to the beginning of the 1990s. These authors argued that the evidence for the superior forecasting performance of nonlinear models is patchy.…”

Section: Preamblementioning

confidence: 99%

25 years of time series forecasting

Gooijer

Hyndman

2006

International Journal of Forecasting

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During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.

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Section: Preamblementioning

confidence: 99%

25 years of time series forecasting

Gooijer

Hyndman

2006

International Journal of Forecasting

Self Cite

1,291

632

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show abstract

“…In general, a good in-sample fit does not necessarily induce a good out-of-sample forecasting performance (Clements et al 2004). However, several authors did find promising evidence in favor of nonlinear models (e.g., De Gooijer &Kumar 1992 andMaravall 1983). Nonetheless, as pointed out by Clements et al (2004) the overall poor forecasting performance of nonlinear models requires further research.…”

Section: Introductionmentioning

confidence: 99%

On the Predictive Content of Nonlinear Transformations of Lagged Autoregression Residuals and Time Series Observations

Rossen

2016

Jahrbücher Für Nationalökonomie Und Statistik

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“…To overcome this problem, neural networks offer the potential for general purpose nonlinear time series forecasting. As stated in [42], a good nonlinear model should be general enough to capture some of the nonlinear phenomena in the data. Neural network load forecasters can be thought of as mappings from a set of previous load, current load and future climatology variables (i.e.…”

Section: Neural Network For Forecastingmentioning

confidence: 99%