A GARCH forecasting model to predict day-ahead electricity prices

By Reinaldo Crispiniano Garcia, Javier Contreras, Marco van Akkeren, Joao Batista C. Garcia. 2005

738 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering

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Citations in the corpus

An Adaptive Wavelet Neural Network-Based Energy Price Forecasting in Electricity Markets (2008, background): “Prediction of LMP based on time series (ARMA and GARCH) and artificial intelligence (MLP, RNN and ANFIS) techniques have been simulated and compared in [17].”
Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques (2007, methodology): “Frequently, but not necessarily, leads to forecasting errors that are close to that of the optimal value.”
Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models (2008, methodology): “The list includes ARIMA and seasonal ARIMA models (Contreras et al., 2003, Zhou et al., 2006), autoregressions with heteroskedastic (Garcia et al., 2005) or heavy-tailed (Weron, 2008) innovations, AR models with exogenous (fundamental) variables – ‘dynamic regression’ (or ARX) and ‘transfer…”
Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models (2006, positive): “A similar result was obtained by Garcia et al. (2005) who studied ARIMA models with GARCH residuals and concluded that ARIMA-GARCH outperforms a generic ARIMA model, but only when high volatility and price spikes are present.”
Electricity price forecasting in deregulated markets: A review and evaluation (2009, methodology): “[38] 147, 105 12 months WMAPE 9–11% 1 DA PP”
Application of Public-Domain Market Information to Forecast Ontario's Wholesale Electricity Prices (2006, methodology): “[7], ARIMA models [2], [7]–[9], multivariate dynamic regression (DR) models [2], [10], multivariate transfer function (TF) models [2], [10], [11], and GARCH models [12] have been applied.”
Day-ahead price forecasting of electricity markets by a new fuzzy neural network (2006, background): “To solve this problem, another kind of time-series model known as autoregressive conditional heterocedastic (ARCH)-type models, assuming time-dependent variance, have been developed [5], [10].”
Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting (2012, methodology): “There are three fundamental techniques which have been applied for such forecasts for a single system: (1) statistical techniques focused on smoothing and averaging such as regression [24], exponential smoothing [8], Kalman filters [17], and stochastic models [29], (2) time series methods such as li”