ALER-TI

ALER-TI: Retrieval-augmented framework for time series imputation via latent embedding alignment

Researchers propose ALER-TI, a retrieval-augmented framework for time series imputation that addresses limitations of architectures relying solely on local temporal context. The core contribution is Latent Embedding Alignment (LEA), which mitigates representation mismatch between corrupted query sequences and complete historical candidates by applying post-hoc masking in latent space, enabling pre-computation and caching of historical embeddings. The framework is model-agnostic and demonstrated to improve strong baseline models across six real-world datasets under varying missing rates.