ALER-TI: Aligned Latent Embedding Retrieval for Time Series Imputation
Deep learning has significantly advanced time series imputation, yet most existing architectures primarily rely on localized temporal context within the corrupted input sequence. This reliance can be limiting in real-world scenarios, where time series often exhibit non-stationary dynamics, weak temporal correlations, and infrequent patterns that are difficult to reconstruct from nearby observations alone. In this paper, we propose ALER-TI, Aligned Latent Embedding Retrieval for Time Series Imputation, a retrieval-augmented framework that explicitly leverages historical patterns to supplement degraded local context for more reliable missing-value reconstruction. The core of ALER-TI is Late...
| Authors | Xuan-Thong Truong, Trung-Kien Le, Tung Kieu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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