EvoFIF: Evolutionary Discovery of Inductive Biases for Forecast-Oriented Time Series Imputation

Time series imputation is widely used as a preprocessing step for forecasting, yet minimizing imputation error does not necessarily improve forecasting performance. Forecast-oriented time series imputation (FOTSI) therefore optimizes imputation for the downstream task, and existing methods are model-centric, since each of them fits a model to one dataset, missingness pattern or forecasting model and has to be retrained whenever the scenario changes. We propose EvoFIF (Evolutionary Forecast-oriented Imputation Framework), which instead discovers reusable configurations of imputation inductive biases by evolutionary search. EvoFIF extracts an initial pool of explicit biases from classical imputation algorithms with a large language model, evolves it by utility-weighted Thompson Sampling with LLM-based mutation and crossover, and verifies every candidate by downstream forecasting utility, objective alignment and computational cost. On nine multivariate datasets, four missingness patterns and four forecasting models, EvoFIF attains the lowest downstream loss, imputes a scenario orders of magnitude faster than every baseline, and its reusability dominates that of the existing frameworks, since one configuration transfers across datasets, patterns, forecasters and horizons without retraining.