DaTS-Mix: Selecting Pretraining Mixtures for Time-Series Foundation Models Using Domain and Temporal Statistics

Domain-aware pretraining-mixture selection has been widely studied for large language models, but its use in time-series foundation model (TSFM) pretraining is less well established. We study this problem and emphasize temporal statistics as a complement to domain information when selecting source proportions. We propose DATS-MIX, which represents each mixture using source proportions, domain shares, and the first and second raw moments of 14 temporal statistics computed under the training sampler. We condition a TabPFN response predictor on outcomes from 128 runs of a 7.07M-parameter forecasting proxy and use it to select a mixture for larger-model pretraining. On later evaluation windows from the same 28 sources in seven domains, the selected mixture reduces geometric-mean forecasting loss by 10.85% relative to size-proportional sampling and by 5.57% relative to a matched selector without temporal statistics, under the same 20k-update budget. The mixture selected with both moment blocks achieves the lowest mean native loss and weighted quantile loss among the evaluated methods; a Mean-only ablation separates the incremental role of the second-moment block. These results support proxy-guided TSFM data-mixture design and highlight the value of temporal statistics beyond source and domain proportions.