Are Neural Models Always Necessary For Irregular Time Series Forecasting?

Irregular multivariate time-series (IMTS) forecasting is commonly assumed to require specialized neural architectures for their asynchronous nature. We audit this assumption by asking how much standard benchmark performance is explained by simple structure already present in the observed history. We introduce the Anchor family, a set of transparent, gradient-free diagnostic controls based on persistence, smoothing, sparse repetition, recurrence, and non-test empirical rule selection. Across global IMTS benchmarks and novel neural baselines under matched protocols, our Anchor variants achieve an optimal standard-window error in every dataset-metric setting, while AutoAnchor remains competitive across lookback-horizon sweeps and paired entity-level tests. Perturbation, thinning, bootstrap, and ablation analyses show that different datasets reward distinctive low-complexity historical structures, and that neural models regain advantages when history is insufficient or temporal configuration becomes demanding. These results suggest that current IMTS benchmarks can overstate the necessity of complex neural representations, thereby motivating stronger non-neural diagnostic controls first. The source code is available at: https://anonymous.4open.science/r/Anchor-Family/.