Neural Networks Prediction and Fault Diagnosis Applied to Stationary and Non Stationary Arma Modeled Time Series

By M. Marseguerra, S. Minoggio, A. Rossi, Enrico Zio. 1992

How researchers see this paper

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

1. Kasun Bandara
2. Christoph Bergmeir
3. Slawek Smyl
4. Fotios Petropoulos
5. Hansika Hewamalage
6. Pedro Lara-Benítez
7. Konstantinos Benidis
8. Bryan Lim

Citations in the corpus

• Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, background): “…suggest that NNs are suitable to effectively model the underlying seasonality and cyclical patterns in time series due to their universal function approximation properties, i.e., their capacity to estimate linear and non-linear functions (Zaiyong Tang et al., 1991; Marseguerra et al., 1992).”
• Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017, background): “, their capacity to estimate linear and non-linear functions (Zaiyong Tang et al., 1991; Marseguerra et al., 1992).”