Linear versus neural network forecasts for European industrial production series

By Saeed Heravi, D. Osborn, Chris Birchenhall. 2004

130 citations recorded by the source corpus.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e2c2c11a8585e233c8984d0b18435af887f8268d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. J. De Gooijer
2. Weizhong Yan
3. Marcelo C. Medeiros
4. Rob J Hyndman
5. Eduardo F. Mendes
6. Ricardo P. Masini
7. Christoph Bergmeir
8. James W. Taylor

Citations in the corpus

• 25 Years of IIF Time Series Forecasting: A Selective Review (2005, result): “However, in contrast to Swanson & White, Heravi et al. (2004) found that linear models produce more accurate forecasts of monthly seasonally unadjusted European industrial production series than ANN models.”
• Toward Automatic Time-Series Forecasting Using Neural Networks (2012, background): “[6] were in favor of modeling neural networks directly on raw data since they had a concern on potential nonlinearity induced by the seasonality adjustment.”
• 25 years of time series forecasting (2006, result): “However, in contrast to Swanson & White, Heravi et al. (2004) found that linear models produce more accurate forecasts of monthly seasonally unadjusted European industrial production series than ANN models.”
• Machine Learning Advances for Time Series Forecasting (2020, background): “Other examples from the early literature are Swanson and White (1995, 1997a, 1997b), Balkin and Ord (2000), Tkacz (2001), Medeiros et al. (2001), and Heravi et al. (2004).”