Judgemental forecasting in times of change

O’Connor, Marcus; Remus, William; Griggs, Kenneth A.

doi:10.1016/0169-2070(93)90002-5

Cited by 109 publications

(50 citation statements)

References 20 publications

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“…One would expect that for the¯at series there would be no evidence of a systematic error or bias Ð the judgemental forecasts are just as likely to be above as below the actual value. However, in the light of the well-documented tendency to dampen trended series (Lawrence and Makridakis, 1989;O'Connor, Remus, and Griggs, 1993), one would expect up series to be underforecast (i.e. the forecast is generally below the actual) and down series to be overforecast.…”

Section: Discussionmentioning

confidence: 96%

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Going Up-Going Down: How Good Are People at Forecasting Trends and Changes in Trends?

1997

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Prior studies of judgemental time-series forecasting have found that people have problems with downward-sloping series. This laboratory-based study presents a controlled experiment of series direction and it investigates the problems of changing trends. Results con®rm that people have signi®cant diculties in dealing with downward-sloping series and that behaviour is consistent with a general tendency to anticipate that downward series will reverse themselves. There is a signi®cantly less tendency to do so for upward series. Results are discussed in terms of systematic and unsystematic error.

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Section: Discussionmentioning

confidence: 96%

“…O'Connor, Remus, and Griggs (1993) used arti®cial time series to examine the performance of people when time series changed. However, even before the time series changed, the performance of people was compared to that of some simple exponential smoothing methods.…”

Section: Introductionmentioning

confidence: 99%

Going Up-Going Down: How Good Are People at Forecasting Trends and Changes in Trends?

1997

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“…Table I shows that Theil's method performed relatively well on the white-noise series and on most of the series involving high levels of noise. Research suggests that judgemental forecasters have diculties in handling noise (Eggleton, 1982;O'Connor et al, 1993) and, as expected, in this experiment the original judgemental forecasts performed relatively poorly on the white-noise and high-noise series. It appears that judgemental forecasters read systematic patterns into random movements in series and hence overreact to recent movements.…”

Section: P Goodwinmentioning

confidence: 59%

Adjusting Judgemental Extrapolations using Theil's Method and Discounted Weighted Regression

Goodwin

1997

J. Forecast.

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Theil's method can be applied to judgemental forecasts to remove systematic errors. However, under conditions of change the method can reduce the accuracy of forecasts by correcting for biases that no longer apply. In these circumstances, it may be worth applying an adaptive correction model which attaches a greater weight to more recent observations. This paper reports on the application of Theil's original method and a discounted weighted regression form of Theil's method (DWR) to the judgemental extrapolations made by 100 subjects in an experiment. Extrapolations were made for both stationary and non-stationary and low-and high-noise series. The results suggest DWR can lead to signi®cant improvements in accuracy where the underlying time-series signal becomes more discernible over time or where the signal is subject to change. Theil's method appears to be most eective when a series has a high level of noise. However, while Theil's corrections seriously reduced the accuracy of judgemental extrapolations for some series the DWR method performed well under a wide range of conditions and never signi®cantly degraded the original forecasts.

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“…For instance, presence of complexity-causing features such as randomness and non-linear trends in time series lead to dysfunctional actions such as overcompensation or confusion (Andreassen and Kraus, 1990). Furthermore, overly difficult time series seem to cause forecasters to ignore cues or to classify important cues as random variations (O'Connor et al, 1993).…”

Section: Introductionmentioning

confidence: 99%

Replication and Extension of a Forecasting Decision Support System: An Empirical Examination of the Time Series Complexity Scoring Technique

Adya¹,

Lusk²

2018

AIS TRR

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Abstract:This study presents a conceptual replication of Adya and Lusk's (2016) forecasting decision support system (FDSS) that identifies the complexity or simplicity of a time series. Prior studies in forecasting have argued convincingly that the design of FDSS should incorporate the complexity of the forecasting task. Yet, there existed no formal way of determining time series complexity until this FDSS, referred to as the Complexity Scoring Technique (CST). The CST uses characteristics of the time series to trigger 12 rules that score the complexity of a time series and classify it along the binary dimension of Simple or Complex. The CST was originally validated using statistical forecasts of a small set of 54 time series as well as judgmental forecasts from 14 representative participants to confirm that the FDSS successfully distinguished Simple series from Complex ones. In this study, we (a) replicate the CST on a much larger set of data from both statistical and judgmental forecasting methods, and (b) extend and validate the series classification categories from the binary Simple-Complex used in the original CST to Very Simple, Simple, Complex, and Very Complex thus adding an ordinal link between the two previous binary designations. Findings suggest that both the replication and extension of the CST further validate it, thereby greatly enhancing its use in the practice of forecasting. Implications for research and practice are discussed.

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Judgemental forecasting in times of change

Cited by 109 publications

References 20 publications

Going Up-Going Down: How Good Are People at Forecasting Trends and Changes in Trends?

Going Up-Going Down: How Good Are People at Forecasting Trends and Changes in Trends?

Adjusting Judgemental Extrapolations using Theil's Method and Discounted Weighted Regression

Replication and Extension of a Forecasting Decision Support System: An Empirical Examination of the Time Series Complexity Scoring Technique

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