Confidence intervals: An empirical investigation of the series in the M-competition

By Spyros Makridakis, M. Hibon, E. Lusk, Moncef Belhadjali. 1987

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

The Tourism Forecasting Competition (2011, background): “Makridakis et al. (1987) conclude that the lower the frequency of the data, the more the coverage probability of the forecast intervals is over-estimated.”
Forecasting for Marketing (2005, unknown): “Some empirical studies have shown that the percentage of actual values that fall outside the 95 percent prediction intervals is substantially greater than 5 percent, and sometimes greater than 50 percent (Makridakis et al., 1987).”
25 Years of IIF Time Series Forecasting: A Selective Review (2005, methodology): “For example, the prediction interval appropriate for a random walk model was applied by Makridakis & Hibon (1987) and Lefrançois (1989) to forecasts obtained from many other methods.”
25 years of time series forecasting (2006, methodology): “For example, the prediction interval appropriate for a random walk model was applied by Makridakis & Hibon (1987) and Lefrançois (1989) to forecasts obtained from many other methods.”
Extrapolation for Time-Series and Cross-Sectional Data (2009, unknown): “Key words: acceleration, adaptive parameters, analogous data, asymmetric errors, base rate, BoxJenkins, combining, conservatism, contrary series, cycles, damping, decomposition, discontinuities, domain knowledge, experimentation, exponential smoothing, functional form, judgmental adjustments, M-comp”