arXiv:2607.07640v1 Announce Type: new Abstract: 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 explicit
The increasing complexity and non-stationary nature of real-world time series data necessitate more robust imputation methods than those relying solely on local context.
Improved time series imputation techniques enhance data quality and reliability for AI models, which is crucial for critical applications across various domains, from finance to infrastructure management.
Traditional deep learning methods for time series imputation, often constrained by local context, may be superseded by retrieval-augmented frameworks that leverage broader data patterns.
- · AI model developers
- · Data analytics companies
- · Healthcare sector
- · Manufacturing sector
- · Legacy time series imputation methods
- · Systems highly reliant on localized temporal context for imputation
More accurate and reliable predictions from AI models trained on time series data.
Reduced operational risks and improved efficiency in industries depending on time series analysis.
Accelerated development of autonomous systems that require robust real-time data handling for decision-making.
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Read at arXiv cs.LG