Computing, Artificial Intelligence and Information Technology Neural network forecasting for seasonal and trend time series
By G. Zhang, Min Qi. 2005
591 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/4ae710cb733d0966b40e22efed49565536f36e0a
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- Christophe Paoli
- Shaohui Ma
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- Marie-Laure Nivet
- Satish T. S. Bukkapatnam
Citations in the corpus
- Performance of state space and ARIMA models for consumer retail sales forecasting (2015, background): “Zhang and Qi [9] and Kuvulmaz et al.”
- Retail forecasting: Research and practice (2019, background): “Thus, time series models have long been applied for market level aggregate retail sales forecasting (e.g., Alon et al., 2001; Bechter and Rutner, 1978; Schmidt, 1979; Zhang and Qi, 2005).”
- Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks (2012, methodology): “…previous studies have proposed hybrid MLP models by incorporating the ARIMA model (Zhang, 2003; Zeng et al., 2008), seasonal ARIMA model (Tseng et al., 2002; Zhang and Qi, 2005), genetic algorithms (Vlahogianni et al., 2005), fuzzy logic rules (Rojas et al., 2008; Zhang and Ye, 2008), and so on.”
- Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm (2012, methodology): “Based on WNN-LM, better performance in function learning [51], including short-term traffic flow forecasting [23], can be obtained than those of the conventional feedforward NNs.”
- A case study on a hybrid wind speed forecasting method using BP neural network (2011, background): “According to Zhang and Qi [34], the classical multiplicative decomposition model is appropriate for many time series with increasing seasonal variations.”
- Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule–Walker equation based approach (2011, background): “…done to smooth the time series which would be exposed to artificial neural network training and it has been already established in the literature (Zhang and Min, 2005; Zhang and Kline, 2007) that removing the seasonal components and trends from a time series has a positive impact upon artificial…”