Anomaly detection in multivariate time series has been attracting attention in order to realize continuous stable operation of systems. As systems diversify and monitoring targets become more complex, the number and types of measurements obtained from sensors in the system have dramatically increased. It is necessary to instantly process a large amount of complex multivariate time series in order to determine anomalies with high detection accuracy. In this paper, we proposed a Transformer with a Discriminator for Anomaly Detection in multivariate time series (TDAD). Introducing an adversarial training and attention mechanisms has improved extractions of detailed loss and time series features during model training.We compare the performance of TDAD with five other deep learning methods on five publicly available datasets and demonstrate that it can determine anomalies with high accuracy. Furthermore, by proposing a TDAD with Sparse attention mechanism (called STDAD), we improved the interpretability of the patterns of time series and achieved better results by increasing the influence of strongly relevant data points in time series with long-term dependencies.