Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models

By Z. Tan, Jinliang Zhang, Jianhui Wang, Jun Xu. 2010

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An improved wavelet–ARIMA approach for forecasting metal prices (2014, positive): “…trade-off between wave-length and smoothness ” without further specifying how the choice relates to the time series characteristics or how it affects the forecast accuracy (Amjady and Keynia, 2008; Conejo et al., 2005; Shafie-khah et al., 2011; Tan et al., 2010, Voronin and Partanen, in press).”
MPC-Based Appliance Scheduling for Residential Building Energy Management Controller (2013, background): “Extensive studies about electricity price forecasting can be found in [14], [15], etc.”
Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting (2012, methodology): “For this reason time series methods such as linear univariate model [9], ARIMA [3], Hagan and Behr [15], in combination with econometrics model [10], GIGARCH [12], GARCH [13] and hybrid models such as combination of ARIMA and GARCH using wavelet transform [28] were not considered for STMLF.”