A dynamic artificial neural network model for forecasting time series events
By Manoochehr Ghiassi, Hassine Saidane, David Zimbra. 2005
272 citations recorded by the source corpus.
Subfields: Artificial Intelligence, 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 Artificial Intelligence
- Jan Gasthaus
- David Salinas
- Valentin Flunkert
- Zhong Fan
- J. Skinner
- Goksel Misirli
- Andrew Cook
- David Zimbra
In Management Science and Operations Research
- Jan Gasthaus
- David Salinas
- Valentin Flunkert
- Zhong Fan
- J. Skinner
- Goksel Misirli
- Andrew Cook
- Jih-Jeng Huang
Citations in the corpus
- DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks (2017, methodology): “Neural networks in forecasting have been typically applied to individual time series, i.e. a different model is fitted to each time series independently [14, 8, 6].”
- Using artificial neural network models in stock market index prediction (2011, background): “Ghiassi, Saidane, and Zimbra (2005) evaluated ANN, ARIMA and DAN2 (Dynamic Architecture for Artificial Neural Networks) using popular time series in literature.”
- Anomaly Detection for IoT Time-Series Data: A Survey (2020, methodology): “With the development of RNNs [66], [67] such as LSTM and GRU the ability for the neural network approach to better model the variability present in complex univariate systems has been demonstrated in [68].”
- Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks (2015, background): “Usually stock prices can be seen as a random time sequence with noise, and a number of analysis methods have utilized artificial neural networks to predict stock price trends [6] [7] [8] [9] [10] [11].”
- Recurrent neural network for dynamic portfolio selection (2006, background): “Although many methods including vector autoregression (VAR) [3,4] and generalize autoregressive conditional heteroshedastic (GARCH) [5–7] has been proposed to deal with the dynamic portfolio selection problem, several restricted assumptions, such as stationary time series, independent variables, and”
- Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network (2013, methodology): “The algorithm uses internally set metrics to avoid overfitting as described in Ghiassi et al. (2005).”