Anomaly Detection with Machine Learning, Deep Learning

• 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.

Originally published at www.xenonstack.com.