Time series forecasting has extensive and significant applications in various fields such as transportation, energy, finance, etc. In recent years, time series foundation models have emerged prominently in the prediction field due to their ability to achieve accurate predictions with minimal fine-tuning or even in zero-shot scenarios. Among these models, TabPFN and its derivative TabPFN-TS stand out as notable examples. This paper introduces a TabPFN-based time series prediction method that capitalizes on the intrinsic periodicity of data. Experimental evaluations across multiple time series datasets reveal that our proposed method outperforms TabPFN-TS. This outcome validates the efficacy of incorporating data periodicity into TabPFN-based time series prediction. The code of our method can be found here: