The Forecast Pro methodology

By R. L. Goodrich. 2000

47 citations recorded by the source corpus.

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The Tourism Forecasting Competition (2011, methodology): “Although the finer details of the model identification, estimation and selection are not revealed, Goodrich (2000) presents some details.”
Automatic Time Series Forecasting: The forecast Package for R (2008, neutral): “In particular, Forecast Pro (Goodrich 2000) is well-known for its excellent automatic ARIMA algorithm which was used in the M3-forecasting competition (Makridakis and Hibon 2000). Another proprietary algorithm is implemented in Autobox (Reilly 2000). Ord and Lowe (1996) provide an early review of so”
Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction (2011, methodology): “…the software expert system ForecastPro using automatic model selection and the parameterisation of exponential smoothing (ES) and ARIMA models (Goodrich, 2000), and Theta, a decomposition approach combining exponential smoothing and regressing around a damped trend line (Assimakopoulos &…”
A brief history of forecasting competitions (2020, methodology): “The algorithm selects between an exponential smoothing and ARIMA model based on some state space approximations and a BIC calculation (Goodrich 2000).”
A comparative study of linear and nonlinear models for aggregate retail sales forecasting (2003, methodology): “The capability of this package is documented in Goodrich (2000).”