Demand Forecasting Intermittent and Lumpy Time Series: Comparing Statistical, Machine Learning and Deep Learning Methods

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dc.contributor.authorKiefer, Daniel
dc.contributor.authorGrimm, Florian
dc.contributor.authorBauer, Markus
dc.contributor.authorVan, Dinther, Clemens
dc.date.accessioned2020-12-24T19:16:16Z
dc.date.available2020-12-24T19:16:16Z
dc.date.issued2021-01-05
dc.description.abstractForecasting intermittent and lumpy demand is challenging. Demand occurs only sporadically and, when it does, it can vary considerably. Forecast errors are costly, resulting in obsolescent stock or unmet demand. Methods from statistics, machine learning and deep learning have been used to predict such demand patterns. Traditional accuracy metrics are often employed to evaluate the forecasts, however these come with major drawbacks such as not taking horizontal and vertical shifts over the forecasting horizon into account, or indeed stock-keeping or opportunity costs. This results in a disadvantageous selection of methods in the context of intermittent and lumpy demand forecasts. In our study, we compare methods from statistics, machine learning and deep learning by applying a novel metric called Stock-keeping-oriented Prediction Error Costs (SPEC), which overcomes the drawbacks associated with traditional metrics. Taking the SPEC metric into account, the Croston algorithm achieves the best result, just ahead of a Long Short-Term Memory Neural Network.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.172
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70784
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIntelligent Decision Support for Logistics and Supply Chain Management
dc.subjectdemand forecasting
dc.subjectintemittent
dc.subjectlumpy
dc.subjectspec
dc.subjectdeep learning
dc.titleDemand Forecasting Intermittent and Lumpy Time Series: Comparing Statistical, Machine Learning and Deep Learning Methods
prism.startingpage1425

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