Time‐series analysis supported by power transformations

By Víctor M. Guerrero. 1993

How researchers see this paper

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Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation (2016, methodology): “In this work, we restrict it to lie in the [0, 1] interval, and then use the method of Guerrero (1993) to choose its value in the following way.”
Exploring the sources of uncertainty: Why does bagging for time series forecasting work? (2018, methodology): “The parameter λ of the transformation is chosen automatically using the procedure of Guerrero (1993).”
Price-Taker Bidding Strategy under Price Uncertainty (2002, methodology): “Formulae to compute parameters and are provided below [34]:”
Application of Public-Domain Market Information to Forecast Ontario's Wholesale Electricity Prices (2006, methodology): “Natural logarithmic transformation is found to be the optimal Box–Cox transformation for variance stability in this paper, given the historical data and the identified models; hence, the forecasts are untransformed using the unbiased untransformation method in [20].”
ARIMA Models to Predict Next-Day Electricity Prices (2002, methodology): “Formulae to compute parameters and are provided below [34]:”
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017, methodology): “The only procedure we are aware of to choose λ automatically is the procedure of Guerrero (1993).”