A comparison of artificial neural network and time series models for forecasting commodity prices

By Nowrouz Kohzadi, Milton S. Boyd, B. Kermanshahi, Iebeling Kaastra. 1996

How researchers see this paper

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

Forecasting Economics and Financial Time Series: ARIMA vs. LSTM (2018, background): “…and Schmidhuber, 1997; Gers, 2000; Yim, 2002; Zhang, 2005; Graves et al., 2009, 2013; Schmidhuber, 2015; Brownlee, 2016; Gamboa, 2017; Roondiwala, 2017] as well as its applications in economics and finance data such as predicting the volatility of the S&P 500 [Kohzadi, 1996; Giles at al.”
Short-term electricity prices forecasting in a competitive market: A neural network approach (2007, background): “Moreover, the success of ARIMA models is conditional upon the underlying data generating process being linear, while neural networks can account for nonlinear relationships [29].”
Combining neural network model with seasonal time series ARIMA model (2002, methodology): “Ansuj et al. [5], Kohzadi et al. [6], Chin and Arthur [7], Hill et al. [8] and Caire et al. [9] all proved neural networks to be a superior method for forecasting in their test cases.”
A Comparison of Neural Networks with Time Series Models for Forecasting Returns on a Stock Market Index (2002, background): “Boyd and Kaastra [2] examined whether neural networks can outperform traditional ARIMA models for forecasting commodity prices.”