Judgmental Time-Series Forecasting Using Domain Knowledge

Webby, Richard; O’Connor, Marcus; Lawrence, Michael

doi:10.1007/978-0-306-47630-3_17

Cited by 17 publications

(12 citation statements)

References 26 publications

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“…This research is of a descriptive quantitative type, with a comparative casual character on the behavioral manifestations of the mother-infant interactive process 18 . The sample consisted of 182 mothers and their babies, these born at term or preterm, at a university hospital in the central area of the Brazilian southern state of Rio Grande do Sul, whose babies passed by the neonatal hearing screening in the period March-June 2010.…”

Section: Methodsmentioning

confidence: 99%

“…Data collection was performed by the psychologist/researcher in a room set aside for this purpose, in the hospital itself, for approximately 30 minutes, through a semi-structured interview about the experience of motherhood, developed and standardized by Schwengber and Piccinini 18 . This interview aims to investigate the obstetric history of mothers and get the socioeconomic, demographic, obstetric and psychosocial data.…”

Section: Methodsmentioning

confidence: 99%

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Constituição da experiência da maternidade e risco ao desenvolvimento infantil

2014

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Constituição da experiência da maternidade e risco ao desenvolvimento infantil

2014

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“…An experiment involving Webby's prototypical FSS, Griffin, showed that forecasters were able to take more information into account and to produce more accurate forecast when they used the decomposition facility available in the system (Webby et al,2001) (this was unavailable in the software used in the case companies).…”

Section: Design Features Of Forecasting Support Systemsmentioning

confidence: 99%

The design features of forecasting support systems and their effectiveness

Fildes

Goodwin

Lawrence

2006

Decision Support Systems

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109

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Forecasts play a key role in the management of the supply chain. In most organisations such forecasts form part of an information system on which other functions such as scheduling, resource planning and marketing depend. Forecast accuracy is, therefore, an important component in the delivery of an effective supply chain. Typically, the forecasts are produced by integrating managerial judgment with quantitative forecasts within a forecasting support system (FSS). However, there is much evidence that this integration is often carried out poorly with deleterious effects on accuracy. This study considers the role that a well-designed FSS might have in improving this situation. It integrates the literatures on forecasting and decision support to explain the causes of the problem and to identify design features of FSSs that might help to ameliorate it. An assessment is made of the extent to which currently available business forecasting packages, which are widely employed in supply chain management, possess these features.

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“…In this study, we used a no-trend model, but other options might be superior. For example, based on evidence summarized by Webby, O'Connor and Lawrence (2001), judgmental extrapolations might be useful.…”

Section: Exhibit 7 Forecasting Of Trends Depends On the Directions Ofmentioning

confidence: 99%

Rule-Based Forecasting: Using Judgment in Time-Series Extrapolation

Armstrong

Adya

Collopy

2001

International Series in Operations Research &Amp; Management Science

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Rule-Based Forecasting (RBF) is an expert system that uses judgment to develop and apply rules for combining extrapolations. The judgment comes from two sources, forecasting expertise and domain knowledge. Forecasting expertise is based on more than a half century of research. Domain knowledge is obtained in a structured way; one example of domain knowledge is managers= expectations about trends, which we call "causal forces. " Time series are described in terms of 28 conditions, which are used to assign weights to extrapolations. Empirical results on multiple sets of time series show that RBF produces more accurate forecasts than those from traditional extrapolation methods or equal-weights combined extrapolations. RBF is most useful when it is based on good domain knowledge, the domain knowledge is important, the series is well behaved (such that patterns can be identified), there is a strong trend in the data, and the forecast horizon is long. Under ideal conditions, the error for RBF's forecasts were one-third less than those for equal-weights combining. When these conditions are absent, RBF neither improves nor harms forecast accuracy. Some of RBF's rules can be used with traditional extrapolation procedures. In a series of studies, rules based on causal forces improved the selection of forecasting methods, the structuring of time series, and the assessment of prediction intervals. Disciplines Business | Marketing ABSTRACTRule-Based Forecasting (RBF) is an expert system that uses judgment to develop and apply rules for combining extrapolations. The judgment comes from two sources, forecasting expertise and domain knowledge. Forecasting expertise is based on more than a half century of research. Domain knowledge is obtained in a structured way; one example of domain knowledge is managers= expectations about trends, which we call "causal forces." Time series are described in terms of 28 conditions, which are used to assign weights to extrapolations. Empirical results on multiple sets of time series show that RBF produces more accurate forecasts than those from traditional extrapolation methods or equal-weights combined extrapolations. RBF is most useful when it is based on good domain knowledge, the domain knowledge is important, the series is well behaved (such that patterns can be identified), there is a strong trend in the data, and the forecast horizon is long. Under ideal conditions, the error for RBF's forecasts were one-third less than those for equal-weights combining. When these conditions are absent, RBF neither improves nor harms forecast accuracy. Some of RBF's rules can be used with traditional extrapolation procedures. In a series of studies, rules based on causal forces improved the selection of forecasting methods, the structuring of time series, and the assessment of prediction intervals.

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Judgmental Time-Series Forecasting Using Domain Knowledge

Cited by 17 publications

References 26 publications

Constituição da experiência da maternidade e risco ao desenvolvimento infantil

Constituição da experiência da maternidade e risco ao desenvolvimento infantil

The design features of forecasting support systems and their effectiveness

Rule-Based Forecasting: Using Judgment in Time-Series Extrapolation

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