What is an Anomaly Detection Algorithm?

Anomaly detection is the process of identifying data points that deviate from the expected patterns in a dataset. Many applications, including fraud detection, intrusion detection, and failure detection, often use anomaly detection techniques. Finding uncommon or very infrequent events that could point to a possible hazard, issue, or opportunity is the aim of anomaly detection.

The autoencoder algorithm is an unsupervised deep learning algorithm that can be used for anomaly detection in time series data. The autoencoder is a neural network that learns to reconstruct its input data By first compressing input data into a lower-dimensional representation and then extending it back to its original dimensions. An autoencoder may be trained on typical time series data to learn a compressed version of the data for anomaly identification. The anomaly score may then be calculated using the reconstruction error between the original and reconstructed data. Anomalies are data points with considerable reconstruction errors.

Anomaly detection is the process of identifying data points or patterns in a dataset that deviate significantly from the norm. A time series is a collection of data points gathered over some time. Anomaly detection in time series data may be helpful in various industries, including manufacturing, healthcare, and finance. Anomaly detection in time series data may be accomplished using unsupervised learning approaches like clustering, PCA (Principal Component Analysis), and autoencoders.