LatentTime: Efficient Long-context Time Series Foundation Models via Latent Forecasting

Time series foundation models (TSFMs) have emerged as a promising approach to general-purpose forecasting. As public pre-training corpora expand, recent TSFMs increasingly combine larger model capacities with longer input contexts. However, fine-grained patching and padding of variable-length sequences introduce substantial computational overhead. To solve this problem, we propose LatentTime, an efficient long-context forecasting framework that combines continuous latent forecasting with a Length-Aware Bucket strategy. Its lightweight TS-AE encoder compresses long input contexts by $2\times$ , enabling forecasting in the resulting latent space. The decoder then performs $2\times$ temporal super-resolution on the latent predictions to restore the original resolution. Together, these operations approximately halve input tokens and double the forecast span per prediction block. The Length-Aware Bucket strategy further reduces padding overhead and controls training exposure across context lengths. Experiments on the GIFT-Eval benchmark demonstrates that LatentTime achieves forecasting accuracy comparable to that of raw-space PatchTST with lower forward FLOPs, while TS-AE maintains high reconstruction fidelity for long contexts across diverse time series.