Prediction Intervals for Time-Series Forecasting

By C. Chatfield. 2001

96 citations recorded by the source corpus.

Subfields: Applied Mathematics, Finance, Management Science and Operations Research

How researchers see this paper

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

In Applied Mathematics

1. Gillian M. Mimmack
2. George Athanasopoulos
3. Simon Mason
4. Rob J Hyndman
5. Sven F. Crone
6. Robert Fildes
7. Konstantinos Nikolopoulos
8. A. Syntetos

In Finance

1. Rob J Hyndman
2. Kevin K. F. Wong
3. George Athanasopoulos
4. Shen Liu
5. Gillian M. Mimmack
6. Simon Mason
7. D. Wu
8. Sven F. Crone

In Management Science and Operations Research

1. Konstantinos Nikolopoulos
2. Rob J Hyndman
3. Haiyan Song
4. Robert Fildes
5. George Athanasopoulos
6. Sven F. Crone
7. Fotios Petropoulos
8. D. Wu

Citations in the corpus

• The Tourism Forecasting Competition (2011, background): “• Previous studies have found the forecast intervals obtained to be too narrow; hence, the actual values fall outside the empirical forecast intervals more often than they should (see for example Makridakis and Winkler, 1989; Chatfield, 2001; Hyndman et al., 2002).”
• Verification tools for probabilistic forecasts of continuous hydrological variables (2006, unknown): “…consists of an interval [ L i (p), U i (p) wherein the future value x i is supposed to lie with coverage probability p , one has an interval forecast (Chatfield, 2001; Christoffersen, 1998); finally, if the whole probability distribution of the predictands, p i ( ˆ x i ) , is estimated, one has a…”
• Comparison of Some Statistical Methods of Probabilistic Forecasting of ENSO. (2002, methodology): “The multiple regression model provides good reliability, including minimal unconditional bias, for all five categories, even though prediction intervals are usually too wide (Chatfield 2001).”
• Judgmental forecasting: A review of progress over the last 25 years (2006, background): “, Chatfield, C., Hibon, M., Lawrence, M., Mills, T., Ord, K., et al. (1993). The M2 competition: A real time judgmentally based forecasting study. International Journal of Forecasting, 9, 5–22. Martino, J. P. (2003). A review of selected recent advances in technological forecasting. Technological Fo”
• Beyond point forecasting: evaluation of alternative prediction intervals for tourist arrivals (2011, background): “Due mainly to these reasons, it is widely believed that prediction intervals are too narrow, under-estimating the degree of future uncertainty (see, for example, Chatfield, 2001, p.487; and Makridakis, Wheelwright, & Hyndman, 1998, p. 470).”
• Forecasting and operational research: a review (2008, methodology): “The adequacy of these theoretical formulae has proved suspect when their predictions of quantiles are compared to observed errors (Chatfield, 2001).”