Fuzzy decision support system for demand forecasting with a learning mechanism

Petrov́ić, Dobrila; Xie, Ying; Burnham, Keith J.

doi:10.1016/j.fss.2006.03.011

Cited by 42 publications

(22 citation statements)

References 16 publications

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“…Authors more often proposed combining expert-generated point estimates compared to predictive distributions. A diverse set of models were proposed to combine point estimates: regression models (linear regression, logistic regression, ARIMA, exponential smoothing), simple averaging, and neural networks (Cabello et al, 2012;Adams et al, 2009;Mak et al, 1996;Graefe et al, 2014b;, and fuzzy logic Petrovic et al (2006); Kabak and Ulengin (2008); Jana et al (2019); Ren-jun and Xian-zhong (2002). Authors that combined predictive densities focused on simpler combination models.…”

Section: Forecasting Methodologymentioning

confidence: 99%

“…Expert forecasts are most readily found in finance, business, and marketing (Seifert and Hadida, 2013;Shin et al, 2013;Franses, 2011;Petrovic et al, 2006;Alvarado-Valencia et al, 2017;Song et al, 2013;Baecke et al, 2017,? ;Petrovic et al, 2006;Franses, 2011;Song et al, 2013;Alvarado-Valencia et al, 2017;Seifert and Hadida, 2013;Kabak andÜlengin, 2008). These fields focus on decision makers and their ability to make predictions from data that cannot easily be collected and fed to a statistical model.…”

Section: Introductionmentioning

confidence: 99%

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Aggregating expert-elicited data for prediction: A scoping review of statistical methods, experiments, and applications v1

McAndrew¹

2019

protocols.io

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Forecasts support decision making in a variety of applications. Statistical models can produce accurate forecasts given abundant training data, but when data is sparse, rapidly changing, or unavailable, statistical models may not be able to make accurate predictions. Expert judgmental forecasts-models that combine expert-generated predictions into a single forecast-can make predictions when training data is limited by relying on expert intuition to take the place of concrete training data. Researchers have proposed a wide array of algorithms to combine expert predictions into a single forecast, but there is no consensus on an optimal aggregation model. This scoping review surveyed recent literature on aggregating expert-elicited predictions. We gathered common terminology, aggregation methods, and forecasting performance metrics, and offer guidance to strengthen future work that is growing at an accelerated pace.

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Section: Forecasting Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aggregating expert-elicited data for prediction: A scoping review of statistical methods, experiments, and applications v1

McAndrew¹

2019

protocols.io

View full text Add to dashboard Cite

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“…Moreover, they highlight that a group-based judgemental adjustment tends to improve the accuracy of both the statistical average of the forecasts and the pure model-based forecast. On this side, providing additional information and different approaches, such as fuzzy modelling, improves the accuracy (Kabak & Ülengin, 2010;Petrovic, Xie, & Burnham, 2006). In addition, in situations, where forecasters might be biased, it is important to obtain forecasts from experts with different biases (Armstrong, Green, & Soon, 2008).…”

Section: Forecasting Methods Integrating Judgemental and Mathematicalmentioning

confidence: 99%

A Collaborative Demand Forecasting Process with Event-Based Fuzzy Judgements

Cheikhrouhou

Marmier

Ayadi³

et al. 2013

SSRN Journal

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a b s t r a c tMathematical forecasting approaches can lead to reliable demand forecast in some environments by extrapolating regular patterns in time-series. However, unpredictable events that do not appear in historical data can reduce the usefulness of mathematical forecasts for demand planning purposes. Since forecasters have partial knowledge of the context and of future events, grouping and structuring the fragmented implicit knowledge, in order to be easily and fully integrated in final demand forecasts is the objective of this work. This paper presents a judgemental collaborative approach for demand forecasting in which the mathematical forecasts, considered as the basis, are adjusted by the structured and combined knowledge from different forecasters. The approach is based on the identification and classification of four types of particular events. Factors corresponding to these events are evaluated through a fuzzy inference system to ensure the coherence of the results. To validate the approach, two case studies were developed with forecasters from a plastic bag manufacturer and a distributor belonging to the food retailing industry. The results show that by structuring and combining the judgements of different forecasters to identify and assess future events, companies can experience a high improvement in demand forecast accuracy.

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“…Its use in expert system for demand forecasting is varied: arithmetic mean, mean absolute error, mean squared error, root mean square error or mean absolute percentage error. Petrovic et al (2006) proposed a decision support system for demand forecasting that combines four forecasts values: two of them represent subjective judgments on future demand and two crisp values obtained using conventional statistical methods. The learning mechanism consider the arithmetic mean of the forecasts errors recorded in the past periods.…”

Section: Forecasting Experts Sytemsmentioning

confidence: 99%

Forecasting model selection through out-of-sample rolling horizon weighted errors

Mula

2011

Expert Systems with Applications

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ElsevierPoler Escoto, R.; Mula, J. (2011) . AbstractDemand Forecasting is an essential process for any firm whether it is a supplier, manufacturer or retailer. A large number of research works about time series forecast techniques exists in the literature, and there are many time series forecasting tools. In many cases, however, selecting the best time series forecasting model for each time series to be dealt with is still a complex problem. In this paper, a new automatic selection procedure of time series forecasting models is proposed. The selection criterion has been tested using the set of monthly time series of the M3 Competition and two basic forecasting models obtaining interesting results. This selection criterion has been implemented in a forecasting expert system and applied to a real case, a firm that produces steel products for construction, which automatically performs monthly forecasts on tens of thousands of time series. As result, the firm has increased the level of success in its demand forecasts.

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Fuzzy decision support system for demand forecasting with a learning mechanism

Cited by 42 publications

References 16 publications

Aggregating expert-elicited data for prediction: A scoping review of statistical methods, experiments, and applications v1

Aggregating expert-elicited data for prediction: A scoping review of statistical methods, experiments, and applications v1

A Collaborative Demand Forecasting Process with Event-Based Fuzzy Judgements

Forecasting model selection through out-of-sample rolling horizon weighted errors

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