Combining Neural Network Forecasts on Wavelet-transformed Time Series

By A. Aussem, Fionn Murtagh. 1997

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Citations in the corpus

Conditional Time Series Forecasting with Convolutional Neural Networks (2017, background): “…work well on noisy series, by discarding in each subsequent layer the noise and extracting only the meaningful patterns, in this way bearing similarities to neural networks which use wavelet transformed time series (i.e. a split in high-and low-frequency components) as input, see e.g. [1], [17].”
Comparison of methods for non-stationary hydrologic frequency analysis: Case study using annual maximum daily precipitation in Taiwan (2017, methodology): “…have also combined wavelet analysis with heuristic algorithms for forecasting and analysis in meteorological and hydrological time series (Aussem and Murtagh, 1997; Aussem et al., 1998; Zheng et al., 2000; Zhang and Dong, 2001; Kim and Valdés, 2003; Saravanan et al., 2008; Hsu and Li,…”
Dilated convolutional neural networks for time series forecasting (2018, background): “…work well on noisy series, by discarding in each subsequent layer the noise and extracting only the meaningful patterns, in this way bearing similarities to neural networks which use wavelet transformed time series (i.e. a split in high-and low-frequency components) as input, see e.g. [1], [17].”
A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting (2008, unknown): “A wavelet will decompose the time series into a range of frequency scales [7], [19], [22].”
An improved wavelet–ARIMA approach for forecasting metal prices (2014, background): “Much research has also concentrated on the combination of artificial neural networks with wavelets (e.g. Amina et al., 2012; Amjady and Keynia, 2008; Aussem and Murtagh, 1997; Bashir and El-Hawary, 2009; Catalão et al., 2011; Chen et al., 2010; Jammazi and Aloui, 2012).”
Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. (2010, unknown): “…have become a tool for analyzing local variation in time series (Torrence and Compo, 1998), and hybrid models have been proposed for forecasting a time-series based on a wavelet transform pre-processing (Aussem and Murtagh, 1997; Aussem et al., 1998; Zheng et al., 2000; Zhang and Dong, 2001).”
Wavelets in Economics and Finance: Past and Future (2002, methodology): “An alternative approach also uses the idea of decomposing the signal to be forecast into its time scale components, but instead uses neural networks on each component to produce an overall forecast after recombining the components, see Aussem and Murtagh (1997) and Aussem et al (1998); the former fo”
NONLINEAR MODEL FOR DROUGHT FORECASTING BASED ON A CONJUNCTION OF WAVELET TRANSFORMS AND NEURAL NETWORKS (2003, methodology): “…have become a common tool for analyzing local variation in time series (Torrence and Compo 1998), and hybrid models have been proposed for forecasting a time series based on a wavelet transform preprocessing (Aussem and Murtagh 1997; Aussem et al. 1998; Zheng et al. 2000; Zhang and Dong 2001).”
The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition (2008, background): “Aussem and Murtagh (1997) discuss a simple strategy aimed at improving neural network prediction accuracy based on the combination of predictions at varying resolution levels of the domain under investigation.”
The contribution of wavelets to the analysis of economic and financial data (1999, methodology): “The next approach is presented in a series of papers by Alex Aussem, Fionn Murtagh and co-workers (see, for example, Aussem & Murtagh 1997; Aussem et al .”