An artificial neural network (p, d, q) model for timeseries forecasting

By Mehdi Khashei, Mehdi Bijari. 2010

750 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Hamid Moeeni
2. Sami ben Jabeur
3. Jean-Laurent Viviani
4. Salma Mefteh-Wali
5. Sven F. Crone
6. Nikolaos Kourentzes
7. Hossein Bonakdari
8. Devon K. Barrow

In Management Science and Operations Research

1. Devon K. Barrow
2. Sven F. Crone
3. Nikolaos Kourentzes
4. Hamid Moeeni
5. Sami ben Jabeur
6. Jean-Laurent Viviani
7. Salma Mefteh-Wali
8. Josefa Mula

Citations in the corpus

• Forecasting gold price with the XGBoost algorithm and SHAP interaction values (2021, positive): “They are one of the most important types of machine learning models, which have been introduced and examined for forecasting commodity prices (Khashei & Bijari, 2010; Lineesh et al., 2010; Parisi et al., 2008).”
• Using artificial neural network models in stock market index prediction (2011, background): “Khashei and Bijari (2010) compaired autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), and Zhang’s hybrid model.”
• Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model (2017, background): “Therefore, linear models do not always provide satisfactory results for complex, real problems (Zhang 2003; Khashei and Bijari 2010).”
• Comparison of ARIMA and NNAR Models for Forecasting Water Treatment Plant’s Influent Characteristics (2018, background): “The ARIMA assumes that the series is generated from a linear process (Khashei and Bijari, 2010).”
• Forecasting model selection through out-of-sample rolling horizon weighted errors (2011, background): “Khashei and Bijari (2010) proposed a hybrid model of artificial neural networks using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks.”
• Clustering algorithms: A comparative approach (2016, background): “Such methodologies are motivated by their widespread application in diagnosis [10], education [11], forecasting [12], and many other domains [13].”
• Neural network ensemble operators for time series forecasting (2014, background): “Their attractive properties have led to the rise of several types of NNs and applications in the literature (for examples, see Connor et al., 1994; Zhang et al., 1998; Efendigil et al., 2009; Khashei and Bijari, 2010).”