For the fact that institutional trading in the financial market is highly misleading to retail investors in the financial market, a trend prediction method based on the impact of institutional trading behaviors was proposed. First, using the time series Matrix Profile (MP) algorithm and taking the stock turnover rate as the cut-in point, a knowledge base of turnover rate fluctuations based on the influence of institutional trading behaviors under motifs with different lengths was constructed. Second, the motif's length, which leads to the high accuracy of the prediction result of the stock to be predicted was determined. Finally, the fluctuation trend of single stock under the influence of institutional trading behaviors was predicted through the knowledge base of this motif's length. In order to verify the feasibility and accuracy of the new method of trend prediction, the method was compared with Auto-Regressive Moving Average (ARMA) model and Long Short Term Memory (LSTM) network, and the Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) evaluation indicators were used to compare the 70 stocks' prediction results of three methods. The analysis of experimental results show that, compared with the ARMA model and the LSTM network, in the prediction of 70 stock price trends, the proposed method has more than 80% of the stock prediction results more accurate.

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.