The effect of additive outliers on the forecasts from ARIMA models

By J. Ledolter. 1989

How researchers see this paper

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

Citations in the corpus

Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors (2018, background): “Similar results can also be obtained through outlier (extreme values, level shifts etc.) detection techniques which eliminate the carry-over effect of the abnormalities on the forecast and the bias in the estimates of the model parameters (Ledolter, 1989).”
25 years of time series forecasting (2006, methodology): “Both Ledolter (1989) and Hotta (1993) analysed the effect of an additive outlier on the forecast intervals when the ARIMA model parameters are estimated.”
Outlier Detection And Estimation In NonLinear Time Series (2005, background): “For example, the impact of outliers on parameter estimation has been studied by Peña (1990), on autoregressive moving-average (ARMA) identification by Deutsch et al. (1990), and the effects on forecasts are addressed by Ledolter (1989) and Chen and Liu (1993a).”
Forecasting: theory and practice (2020, methodology): “The influence of different types of outliers on the prediction errors in conditional mean models (ARIMA models) is studied by Chen and Liu (1993a,b) and Ledolter (1989, 1991), while the GARCH context is explored by Franses and Ghijsels (1999) and Catalán and Trı́vez (2007).”
Forecasting Exchange Rates: A Robust Regression Approach (2007, background): “that outliers can account for some nonlinearities detected in the data. Ledolter (1989)”
25 Years of IIF Time Series Forecasting: A Selective Review (2005, background): “Both Ledolter (1989) and Hotta (1993) analysed the effect of the additive outlier on the forecast intervals when the ARIMA model parameters are estimated.”