Doing More with Less: Dense Tokenization for Recurrent Monthly Forecasting

Many forecasting tasks rely on monthly observations, either because finer observations do not exist or because figures are reconciled only at the end of each period. We ask whether changing how these observations are represented can improve a model's accuracy. We propose flat monthly tokenization, which repeats each monthly average across several tokens that carry known calendar features. This representation enables multiple recurrent updates per observation while preserving the information in the monthly history and calendar. On monthly tasks built from two public daily datasets, a small GRU with 90 tokens per month reduces CRPS relative to the monthly baseline by 20 to 49% on retail and 28 to 36% on electricity. On retail, it also achieves lower CRPS than N-HiTS and the same network trained on daily observations. Ablations indicate contributions from the input representation and repeated recurrent updates, with an additional contribution from varying calendar features on electricity. Decomposing the score into median and nonmedian components shows that increasing density from 30 to 90 tokens improves both components on electricity, whereas on retail the improvement is confined to the nonmedian component. These results identify token density as a useful modeling choice for monthly forecasting. On the two evaluated datasets, dense monthly tokenization recovers or exceeds the accuracy of the same network trained on daily observations, using only monthly observations and known calendar features.