Time Series Anomaly Detection with Deep Learning

Getting Started with Anomaly Detection

Anomaly detection is the identification of data points, items, observations or situations that do not correspond to the familiar pattern of a given group. Certain anomalies happen very rarely but may imply a large and significant threat such as cyber intrusions or fraud in the field of IT infrastructure.

Anomaly detection is used for behavioral analysis and other forms of analysis in order to serve in learning about the detection, identification, and prediction of the phenomenon of these anomalies. Let’s take a look at below points to under the uses of anomaly detection in different fields.

  • Anomaly Detection identifies any unusual behavior or pattern in a dataset, used in many applications like Fraud Detection in Banking Sector, Pattern Analysis of Network Traffic, Predictive Maintenance, and Monitoring.
  • Offering AI-powered Log Analytics solutions for Anomaly Detection, finding a correlation between anomalies and predicting anomaly in the IT Infrastructure using Machine Learning and Deep Learning.


Challenge for Building Anomaly Detection System


  • Extensive usage of data growth on a daily basis with the evolution of technology.
  • Increased occurrence of unusual behavior or fraud activities.
  • Need for detection promptly to perform maintenance and achieve monitoring effectively.


Solution for Building Anomaly Detection System with Deep Learning


Guide to Data Preprocessing


Load dataset, store in the object and check datatype of the dataset and convert into float values. After conversion, calculate the total number of hours from date and time and converted dataset loaded as a series.


Overview of Data Wrangling


Plot and visualize time series data. To get the values of AR, I and MA plotting of autocorrelation and description of residuals are necessary.


Understanding Model Implementation


Implement the ARIMA model and predict values obtained and calculate forecast errors. Calculate the mean and standard deviation of the dataset, and compute the anomalies.

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