Skipmodeller

Skipmodeller is a term used in data science and sequence modeling to describe a family of models that explicitly account for skipped or missing elements within sequential data. The central idea is to capture both observed events and the pattern of omissions, which can carry meaningful information in irregularly sampled or incomplete datasets.

In practice, skipmodellers represent input sequences with skip indicators or masks. They may extend standard sequence models such as recurrent neural networks, temporal convolution, or transformers with skip-aware mechanisms, such as masking, conditional generation, or explicit skip variables. Training typically uses likelihood-based objectives under the mask, or reconstruction-based losses, sometimes with imputation components. The goal is to enable the model to predict both observed events and the likely structure of the skipped parts.

Applications of skipmodellers span areas where data may be imperfect or irregular. Examples include music information retrieval with missing notes or rests, sensor networks with irregular sampling, time-series forecasting with gaps, and handwriting or speech recognition with incomplete observations. The approach can improve robustness to missing data and reveal patterns that depend on when data are omitted rather than observed.

Limitations include handling highly variable or non-random skip patterns, potential biases from missing-not-at-random mechanisms, and the need for appropriate evaluation metrics that reflect both observed and imputed components. There is no universally standardized definition for skipmodellers; the concept appears in various research contexts under different implementations and terminology. Related topics include missing data handling, sequence modeling, and imputation.