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SeqOAE: Deep sequence-to-sequence orthogonal auto-encoder for time-series forecasting under variable population sizes
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DOI：10.1016/j.ress.2024.110107.png)
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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.
Keyword:
Auto-encoder
Deep learning
Forecasting
Time-series
Orthogonality
Machine learning
Reliability
Warranty
期刊
R
IF:
11
论文数:
9.0K
被引数:
4.2W
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