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Anomaly detection through data analysis helps to accurately identify abnormal behaviors, improving service quality and decision-making capabilities. However, due to the temporal and spatial dependence of multi-dimensional time series data and the randomness of ab normal events, the existing methods still have certain limitations. Regarding the issue above, this paper proposed a multi-dimensional time series data anomaly detection method MBCLE, which combined new statistical methods and bidirectional convolutional LSTM. The method introduced a stacked median filter to handle point anomalies in the input data and smooth data fluctuations, and designed a neural network predictor combining Bi-ConvLSTM and Bi-LSTM for data modeling and prediction. It smoothed the prediction errors using bidirectional recurrent exponentially weighted moving average ( BrEWMA) . The method used dynamic threshold to calculate the threshold to detect contextual anomalies. The experimental results show that MBCLE has good detection performance and each step contributes to the performance improvement. [ABSTRACT FROM AUTHOR]