Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70784

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

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Title:Demand Forecasting Intermittent and Lumpy Time Series: Comparing Statistical, Machine Learning and Deep Learning Methods
Authors:Kiefer, Daniel
Grimm, Florian
Bauer, Markus
Van, Dinther, Clemens
Keywords:Intelligent Decision Support for Logistics and Supply Chain Management
demand forecasting
intemittent
lumpy
spec
show 1 moredeep learning
show less
Date Issued:05 Jan 2021
Abstract:Forecasting 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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/70784
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.172
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Intelligent Decision Support for Logistics and Supply Chain Management


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