Journal article
Geometric series representation for robust bounds of exponential smoothing difference between protected and confidential data
Annals of operations research, 10479
08 Sep 2023
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Exponential smoothing is one of the most widely used forecasting methods for univariate time series data. Based on the difference between protected and confidential time series data, we derive theoretical bounds for the absolute change to forecasts generated from additive exponential smoothing models. Given time series data up to time t, we discover a functional form of robust bounds for the absolute change to forecasts for any T >= t + 1, which can be represented as a compact form of geometric series. We also find robust bounds for the Change in Mean Absolute Error (Delta MAE) and Measured Mean Absolute Error (MMAE).
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Details
- Title
- Geometric series representation for robust bounds of exponential smoothing difference between protected and confidential data
- Creators
- Jinwook Lee - Drexel UniversityMatthew J. Schneider - Drexel University
- Publication Details
- Annals of operations research, 10479
- Publisher
- Springer Nature
- Number of pages
- 11
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:001061574300001
- Scopus ID
- 2-s2.0-85170051897
- Other Identifier
- 991021852021604721
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- Web of Science research areas
- Operations Research & Management Science