A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction
By S. Ho, Min Xie, Thong Ngee Goh. 2002
324 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Mechanics of Materials, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/035a4e720a6032f46acc6562f8c90462beb6115f
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In Control and Systems Engineering
In Mechanics of Materials
In Safety, Risk, Reliability and Quality
Citations in the corpus
- ARIMA forecasting of primary energy demand by fuel in Turkey (2007, methodology): “…analysis, has been originated from the autoregressive model (AR), the moving average model (MA) and the combination of the AR and MA, the ARMA models (Blanchard and Desrochers, 1984; Brown et al., 1984; Kamal and Jafri, 1997; Ho and Xie, 1998; Saab et al., 2001; Zhang, 2001; Ho et al., 2002).”
- 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 of demand using ARIMA model (2018, methodology): “Another study was done by Ho et al.(7) using simulated failure time of a compressor to determine the more accurate forecasting model.”