Temporal Embedding Stability in Sequence Learning Models

Abstract

Temporal embeddings are central to how sequence learning models represent evolving input patterns
over time. However, these embeddings can shift, stabilize, or drift in ways that directly impact
generalization and reliability. This article investigates temporal embedding stability across recurrent,
convolutional, and transformer-based sequence learning architectures. Using controlled synthetic and
real-world temporal datasets, embeddings were captured at multiple training checkpoints and analyzed
using cosine similarity, Euclidean drift metrics, and temporal alignment evaluation. Results show that
gated recurrent models maintain stable representations in predictable environments, while temporal
convolutional networks exhibit consistently low drift but reduced flexibility under irregular fluctuations.
Transformer models initially display higher embedding drift yet converge to robust stability when
handling dynamic and noise-influenced temporal patterns. The study concludes that model selection for
temporal tasks must account for the nature of temporal variability, and hybrid architectures may offer
balanced trade-offs between embedding stability and expressive adaptability.