Integrating human judgement into quantitative forecasting methods: A review

Arvan, Meysam; Fahimnia, Behnam; Reisi, Mohsen; Siemsen, Enno

doi:10.1016/j.omega.2018.07.012

Cited by 82 publications

(80 citation statements)

References 124 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Unfortunately, the research done in the field of forecasting for implementing bias adjustments is rather limited. Most of the research focuses on judgmental adjustments [46, 47] which are not applicable in our case, or model-specific parameter corrections [48] that can not be adopted within generalised forecasting frameworks. However, [49] describe some practical alternatives, indicating some simple, yet effective approaches for performing this task.…”

Section: Strategies For Improving the Performance Of Temporal Hieramentioning

confidence: 99%

Improving the forecasting performance of temporal hierarchies

2019

View full text Add to dashboard Cite

Temporal hierarchies have been widely used during the past few years as they are capable to provide more accurate coherent forecasts at different planning horizons. However, they still display some limitations, being mainly subject to the forecasting methods used for generating the base forecasts and the particularities of the examined series. This paper deals with such limitations by considering three different strategies: (i) combining forecasts of multiple methods, (ii) applying bias adjustments and (iii) selectively implementing temporal hierarchies to avoid seasonal shrinkage. The proposed strategies can be applied either separately or simultaneously, being complements to the method considered for reconciling the base forecasts and completely independent from each other. Their effect is evaluated using the monthly series of the M and M3 competitions. The results are very promising, displaying lots of potential for improving the performance of temporal hierarchies, both in terms of accuracy and bias.

show abstract

Section: Strategies For Improving the Performance Of Temporal Hieramentioning

confidence: 99%

Improving the forecasting performance of temporal hierarchies

2019

View full text Add to dashboard Cite

show abstract

“…We contribute to four main bodies of research. Management science researchers have recognized the potential value in integrating human judgment with forecasting algorithms (see Arvan et al 2019 for a review). Humans often possess so-called "domain knowledge": better and more up-to-date information than what statistical models use (see §3 of Lawrence et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Eliciting Human Judgment for Prediction Algorithms

2021

View full text Add to dashboard Cite

Even when human point forecasts are less accurate than data-based algorithm predictions, they can still help boost performance by being used as algorithm inputs. Assuming one uses human judgment indirectly in this manner, we propose changing the elicitation question from the traditional direct forecast (DF) to what we call the private information adjustment (PIA): how much the human thinks the algorithm should adjust its forecast to account for information the human has that is unused by the algorithm. Using stylized models with and without random error, we theoretically prove that human random error makes eliciting the PIA lead to more accurate predictions than eliciting the DF. However, this DF-PIA gap does not exist for perfectly consistent forecasters. The DF-PIA gap is increasing in the random error that people make while incorporating public information (data that the algorithm uses) but is decreasing in the random error that people make while incorporating private information (data that only the human can use). In controlled experiments with students and Amazon Mechanical Turk workers, we find support for these hypotheses. This paper was accepted by Charles Corbett, operations management.

show abstract

“…There are two major forecasting methods. First, the qualitative forecasting method is analyzed based on the opinions, judgments, and past performance of forecasting experts (Arvan et al, 2019). The factors that influence demands for commercial products are sales promotions, the introduction of a new brand, store reform, and aggressive marketing (Meneghini et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the time series technique was selected for use in this study. Moreover, the experience level of the forecasters will affect the results of their forecasting: the greater the forecaster's expertise, the greater the adjustment will correctly represent the real insights and improve forecast accuracy (Trapero et al, 2015;Arvan et al, 2019;Fildes et al, 2019). The combination of qualitative forecasting via the adjustment of sales data by experts as well as quantitative forecasting via time series using a software package could increase forecast accuracy by 46.14%, 22.53%, and 56.42% for three kinds of instant noodle products (Khamphinit and Ongkunaruk, 2016).…”

Section: Introductionmentioning

confidence: 99%

Utilizing an Intervention Forecasting Approach to Improve Reefer Container Demand Forecasting Accuracy: A Case Study in Indonesia

Pradita¹,

Ongkunaruk²,

Leingpibul³

2020

IJTech

View full text Add to dashboard Cite

The demand for reefer containers in Indonesia has been increasing due to both global and regional trade growth; however, logistics providers are still struggling with several related challenges, including a container shortage problem, which is due to ineffective forecasting practices. This study aimed to improve the accuracy of reefer container demand forecasting by introducing an intervention forecasting approach. This approach will help address the demand planning issue of reefer circulation. The intervention forecasting approach combines human insights from the qualitative approach with the mathematical precision of the quantitative approach in iterative sequences. This field study was conducted with an Indonesian third party logistic company in Eastern Indonesia. The training data set was analyzed to provide a pattern of demand as well as some initial forecasting parameters (such as trend and seasonal index). Then, an expert helped identify irregular demand points. The demand data was then adjusted by a sales and marketing manager according to related factors such as natural disasters, oil price increase, promotions. The selected models were then further verified using a testing dataset, and the forecast errors from various models using the raw and adjusted training data sets were compared with those of the testing datasets. The results revealed that the mean average percentage error (MAPE) after adjusting the demand was 5.43% to 6.22% for the training and 9.55% to 10.33% for the testing dataset, which is lower than that of the traditional forecasting method when there was no intervention. In summary, the adjustment forecast could increase forecast accuracy by 42.39% and 39.42% for 20-and 40-feet containers, respectively.

show abstract

Integrating human judgement into quantitative forecasting methods: A review

Cited by 82 publications

References 124 publications

Improving the forecasting performance of temporal hierarchies

Improving the forecasting performance of temporal hierarchies

Eliciting Human Judgment for Prediction Algorithms

Utilizing an Intervention Forecasting Approach to Improve Reefer Container Demand Forecasting Accuracy: A Case Study in Indonesia

Contact Info

Product

Resources

About