SeqOAE: Deep sequence-to-sequence orthogonal auto-encoder for time-series forecasting under variable population sizes

Abdallah Chehade, Wael Hassanieh and Vasiliy Krivtsov

Reliability Engineering and System Safety, 2024, vol. 247, issue C

Abstract: This paper presents a novel approach to modeling non-linear time-series data in which the population size changes over observation time. This is a common phenomenon when forecasting the time-series of an ongoing study/operation. The goal is to make predictions about the mature time-series at the end of the study/operation given the immature time-series at an early stage of the study/operation. Existing time-series models often ignore this phenomenon by focusing on a fixed population size or a single unit of interest. This paper proposes a sequence-to-sequence deep learning model (SeqOAE) that captures the change in the time-series as a function of observation time. It learns an orthogonal latent feature space for robust long-term temporal forecasting. SeqOAE is non-parametric in time, which allows it to capture non-trivial and non-linear temporal patterns. SeqOAE enables forecasting performance behavior, which is paramount for reliability engineering applications. The model performance is validated on a reliability engineering case study using an extensive automotive warranty dataset with over 12,500 components from millions of vehicles.

Keywords: Auto-encoder; Deep learning; Forecasting; Time-series; Orthogonality; Machine learning; Reliability; Warranty (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832024001819
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:247:y:2024:i:c:s0951832024001819

DOI: 10.1016/j.ress.2024.110107

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().