This GitHub repository contains the code implementation and resources for a comprehensive Time Series Data Anomaly Detection project.
Overview:- Anomaly detection in time series data is a critical task for many industries, especially in manufacturing, where identifying abnormal behavior can prevent potential equipment failures and downtime. This project addresses the challenge of detecting anomalies in sensor data collected from various machines on a factory floor. The goal is to build an AI-based solution using Azure's time series capabilities for efficient and accurate anomaly detection.
Key Features:
Exploratory Data Analysis (EDA): Uncover insights from the time series dataset, analyze statistical properties, and visualize sensor readings to identify patterns or anomalies.
Preprocessing: Handle missing values, smooth noisy data, and apply transformations or scaling techniques to prepare the data for anomaly detection.
Feature Engineering: Extract relevant features using techniques such as lagging, rolling statistics, and Fourier transformations to capture temporal patterns that facilitate anomaly detection.
Machine Learning Model: Select and implement an appropriate machine learning model, such as autoencoders, LSTM networks, or other suitable models, to identify anomalies effectively.
Model Training and Evaluation: Split the dataset into training and validation sets, train the model, and tune hyperparameters. Use appropriate evaluation metrics to assess model performance.
Anomaly Detection: Apply the trained model to detect anomalies in the test set, identifying timestamps and specific sensor features where anomalies are detected. Visualize and interpret the results for effective communication.
Deployment and Monitoring: Design a pipeline for real-time or near real-time data to automate the anomaly detection process. Demonstrate how to deploy the model in an Azure environment and monitor its performance over time.
Tech Stack::-
Python, Jupyter Notebook, Data Visualization Libraries (e.g., Matplotlib, Seaborn), Machine Learning Libraries (e.g., scikit-learn, TensorFlow).