Forecasting support systems for the incorporation of event information: An empirical investigation

Webby, Richard; O’Connor, Marcus; Edmundson, Bob

doi:10.1016/j.ijforecast.2004.10.005

Cited by 43 publications

(21 citation statements)

References 37 publications

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“…found that estimating the components independently resulted in superior performance compared with producing judgmental forecasts directly. In another study, Webby et al (2005) observed similar results when the effects of special events were estimated separately. Also, Lee and Siemsen (2017) demonstrated the value of task decomposition on order decisions, especially when coupled with decision support.…”

Section: Literaturesupporting

confidence: 57%

Judgmental selection of forecasting models

Petropoulos

Kourentzes

Νικολόπουλος

et al. 2018

J of Ops Management

135

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In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also examined the efficacy of a judgmental model‐build approach, in which experts were asked to decide on the existence of the structural components (trend and seasonality) of the time series instead of directly selecting a model from a choice set. Our behavioral study used data from almost 700 participants, including forecasting practitioners. The results from our experiment suggest that selecting models judgmentally results in performance that is on par, if not better, to that of algorithmic selection. Further, judgmental model selection helps to avoid the worst models more frequently compared to algorithmic selection. Finally, a simple combination of the statistical and judgmental selections and judgmental aggregation significantly outperform both statistical and judgmental selections.

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Section: Literaturesupporting

confidence: 57%

Judgmental selection of forecasting models

Petropoulos

Kourentzes

Νικολόπουλος

et al. 2018

J of Ops Management

135

View full text Add to dashboard Cite

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“…Forecasting integrating the expert judgment has mainly to deal with three research work families: a-the modification of the historical data series to take into account the knowledge of the contextual information, b-the achievement of forecast which can be based on results of mathematical approaches and c-the adjustment of the forecasts to anticipate the impact of known events (Webby et al 2005). Webby and O"connor (1996) define four main types of approaches integrating human judgment into structured forecasting methods: the model building approach is a methodology which uses judgment in the selection and development of the quantitative forecast (Bunn and Wright 1991).…”

Section: Combined Judgemental-mathematical Forecasting Systemsmentioning

confidence: 99%

Structuring and integrating human knowledge in demand forecasting: a judgemental adjustment approach

Marmier

Cheikhrouhou

2010

Production Planning & Control

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Demand forecasting consists of using data of the past demand to obtain an approximation of the future demand. Mathematical approaches can lead to reliable forecasts in deterministic context through extrapolating regular patterns in time-series. However, unpredictable events that do not appear in the historical data can make the forecasts obsolete. Since forecasters have a partial knowledge of the context and of the future events (such as strikes, promotions) with some probability, the idea presented in this work is on structuring the implicit as well as the explicit knowledge in order to easily and fully integrate it in final forecasts. This paper presents a judgemental-based approach in forecasting where mathematical forecasts are considered as a basis and the structured knowledge of the experts is provided to adjust the initial forecasts. This is achieved using the identification and classification of four factors characterising events that could not be considered in the initial forecasts. Validation of the approach is provided with two case studies developed with forecasters from a plastic bag manufacturer and a distributor acting in the food market. The results show that structuring the expert knowledge through the identification of factor-related events lead to high improvements of forecasts accuracy.

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“…33 Findings from numerous domains such as auditing 34 and consumer choice 35 confirm that task complexity results in lower decision accuracy. There are, however, some indications that expertise and environmental factors can reverse these effects.…”

Section: Time Series Complexitymentioning

confidence: 90%

Development and validation of a rule-based time series complexity scoring technique to support design of adaptive forecasting DSS

Adya

Lusk

2016

Decision Support Systems

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Abstract:Evidence from forecasting research gives reason to believe that understanding time series complexity can enable design of adaptive forecasting decision support systems (FDSSs) to positively support forecasting behaviors and accuracy of outcomes. Yet, such FDSS design capabilities have not been formally explored because there exists no systematic approach to identifying series complexity. This study describes the NOT THE PUBLISHED VERSION; this is the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation at the bottom of the page.Decision Support Systems, Vol 83 (March 2016): pg. 70-82. DOI. This article is © Elsevier and permission has been granted for this version to appear in e-Publications@Marquette. Elsevier does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Elsevier.2 development and validation of a rule-based complexity scoring technique (CST) that generates a complexity score for time series using 12 rules that rely on 14 features of series. The rule-based schema was developed on 74 series and validated on 52 holdback series using well-accepted forecasting methods as benchmarks. A supporting experimental validation was conducted with 14 participants who generated 336 structured judgmental forecasts for sets of series classified as simple or complex by the CST. Benchmark comparisons validated the CST by confirming, as hypothesized, that forecasting accuracy was lower for series scored by the technique as complex when compared to the accuracy of those scored as simple. The study concludes with a comprehensive framework for design of FDSS that can integrate the CST to adaptively support forecasters under varied conditions of series complexity. The framework is founded on the concepts of restrictiveness and guidance and offers specific recommendations on how these elements can be built in FDSS to support complexity.

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Forecasting support systems for the incorporation of event information: An empirical investigation

Cited by 43 publications

References 37 publications

Judgmental selection of forecasting models

Judgmental selection of forecasting models

Structuring and integrating human knowledge in demand forecasting: a judgemental adjustment approach

Development and validation of a rule-based time series complexity scoring technique to support design of adaptive forecasting DSS

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