  Anomalies refer to data points or events that deviate from normal and
homogeneous events, which can include fraudulent activities, network
infiltrations, equipment malfunctions, process changes, or other significant
but infrequent events. Prompt detection of such events can prevent potential
losses in terms of finances, information, and human resources. With the
advancement of computational capabilities and the availability of large
datasets, anomaly detection has become a major area of research. Among these,
anomaly detection in time series has gained more attention recently due to the
added complexity imposed by the time dimension. This study presents a novel
framework for time series anomaly detection using a combination of
Bidirectional Long Short Term Memory (Bi-LSTM) architecture and Autoencoder.
The Bi-LSTM network, which comprises two unidirectional LSTM networks, can
analyze the time series data from both directions and thus effectively discover
the long-term dependencies hidden in the sequential data. Meanwhile, the
Autoencoder mechanism helps to establish the optimal threshold beyond which an
event can be classified as an anomaly. To demonstrate the effectiveness of the
proposed framework, it is applied to a real-world multivariate time series
dataset collected from a wind farm. The Bi-LSTM Autoencoder model achieved a
classification accuracy of 96.79% and outperformed more commonly used LSTM
Autoencoder models.
