Multi-task Learning (MTL) makes a positive difference in many fields by improving the prediction effects of correlated tasks among multiple related data sets. Some financial Multivariate Time Series (MTS) also have a high correlation, but applications like price synchronous prediction based on MTL still lack enough attention from researchers. The future values of a certain price are not only related to its own historical values, but also related to other correlated price sequences, and this is a suitable condition for applying the MTL model. This paper constructs an MTL model for synchronous learning and predicting price time series based on the Sequence-to-Sequence (Seq2Seq) model. To obtain enough data for modeling, the Weighted Soft-dtw Barycentric Averaging (wDBA) is used as the Data Augmentation (DA) method to generate more time series data for each Forex pair based on its original OHLC bid quotes. On the testing of 8 Forex pairs’ minute-level quote data from 2020 to 2022, our model, Seq2Seq with DA, outperforms baseline models including the single Long Short-term Memory (LSTM) and Seq2Seq without DA. During the experiment, to provide a comprehensive evaluation on such a long time sequence, more than 1 million minutes, we design the Chronological Randomly-sampling Walk-forward (CRSWF) Validation for a quick evaluation. As a result, when the DA degree is 125%, on the MAE, NMAE, RMSE, and NRMSE, our model respectively reduces by 95.23%, 97.24%, 94.07%, and 95.99% than LSTM, and also reduces by 7.87%, 4.84%, 6.05%, and 1.71% than the Seq2Seq without DA.