Aiming at the low efficiency of traditional anomaly detection methods in processing incremental time series, an Time Series Anomaly Detection method based on frequent pattern discovery (TSAD) was proposed. Firstly, the historical input time series data were transformed into symbols. Secondly, the frequent patterns of historical sequence data sets were found by symbolic features. Finally, the similarity between the frequent pattern and the current new time series data was measured with the longest common subsequence matching method, the abnormal patterns in the newly added data were found. Compared with Time Series Outlier Detection based on sliding window prediction (TSOD) and Extended Symbolic Aggregate Approximation based anomaly mining of hydrological time series (ESAA), the detection rate of TSAD is more than 90% for the three types of time series data selected by the experiment. TSOD has a higher detection rate for more regular sequences, and can reach 99%. But the detection rate of noisy sequences is lower, and the data bias is stronger; and the data detection rate of three types of ESAA is not more than 70%. The experimental results show that TSAD can detect abnormal patterns of time series well.