Premium
Automatic monitoring of forecast errors
Author(s) -
Gardner Everette S.
Publication year - 1983
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980020103
Subject(s) - cusum , exponential smoothing , autocorrelation , smoothing , computer science , signal (programming language) , variance (accounting) , series (stratigraphy) , tracking (education) , forecast error , statistics , exponential function , algorithm , econometrics , mathematics , psychology , paleontology , pedagogy , accounting , business , biology , programming language , mathematical analysis
This paper evaluates a variety of automatic monitoring schemes to detect biased forecast errors. Backward cumulative sum (cusum) tracking signals have been recommended in previous research to monitor exponential smoothing models. This research shows that identical performance can be had with much simpler tracking signals. The smoothed‐error signal is recommended for α = 0.1, although its performance deteriorates badly as α is increased. For higher α values, the simple cusum signal is recommended. A tracking signal based on the autocorrelation in errors is recommended for forecasting models other than exponential smoothing, with one exception. If the time series has a constant variance, the backward cusum should give better results.