Censored Data Forecasting: Applying Tobit Exponential Smoothing with Time Aggregation Article Swipe
Diego J. Pedregal
,
Juan R. Trapero
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.05412
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.05412
This study introduces a novel approach to forecasting by Tobit Exponential Smoothing with time aggregation constraints. This model, a particular case of the Tobit Innovations State Space system, handles censored observed time series effectively, such as sales data, with known and potentially variable censoring levels over time. The paper provides a comprehensive analysis of the model structure, including its representation in system equations and the optimal recursive estimation of states. It also explores the benefits of time aggregation in state space systems, particularly for inventory management and demand forecasting. Through a series of case studies, the paper demonstrates the effectiveness of the model across various scenarios, including hourly and daily censoring levels. The results highlight the model's ability to produce accurate forecasts and confidence bands comparable to those from uncensored models, even under severe censoring conditions. The study further discusses the implications for inventory policy, emphasizing the importance of avoiding spiral-down effects in demand estimation. The paper concludes by showcasing the superiority of the proposed model over standard methods, particularly in reducing lost sales and excess stock, thereby optimizing inventory costs. This research contributes to the field of forecasting by offering a robust model that effectively addresses the challenges of censored data and time aggregation.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.05412
- https://arxiv.org/pdf/2409.05412
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403584384
All OpenAlex metadata
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https://openalex.org/W4403584384Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.05412Digital Object Identifier
- Title
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Censored Data Forecasting: Applying Tobit Exponential Smoothing with Time AggregationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-09-09Full publication date if available
- Authors
-
Diego J. Pedregal, Juan R. TraperoList of authors in order
- Landing page
-
https://arxiv.org/abs/2409.05412Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2409.05412Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2409.05412Direct OA link when available
- Concepts
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Tobit model, Exponential smoothing, Econometrics, Exponential function, Smoothing, Exponential growth, Statistics, Computer science, Economics, Mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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