Foundation models have become popular in forecasting due to their ability to make accurate predictions, even with minimal fine-tuning on specific datasets. In this paper, we demonstrate how the newly released regression variant of TabPFN, a general tabular foundation model, can be applied to time series forecasting. We propose a straightforward approach, TabPFN-TS, which pairs TabPFN with simple feature engineering to achieve strong forecasting performance. Despite its simplicity and with only 11M parameters, TabPFN-TS significantly outperforms Chronos-Small, a model of similar size, and matches or even slightly outperform Chronos-Large, which has 65-fold more parameters. A key strength of our method lies in its reliance solely on artificial data, avoiding the need for large training datasets and eliminating the risk of benchmark contamination.