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Anomaly Detection in Time Series Data with Keras
Snehan Kekre
Snehan Kekre
In this hands-on introduction to anomaly detection in time series data with Keras, you and I will build an anomaly detection model using deep learning. Specifically, we will be designing and training an LSTM autoencoder using the Keras API with Tensorflow 2...
Read more
In this hands-on introduction to anomaly detection in time series data with Keras, you and I will build an anomaly detection model using deep learning. Specifically, we will be designing and training an LSTM autoencoder using the Keras API with Tensorflow 2...
Read more
In this hands-on introduction to anomaly detection in time series data with Keras, you and I will build an anomaly detection model using deep learning. Specifically, we will be designing and training an LSTM autoencoder using the Keras API with Tensorflow 2 as the backend to detect anomalies (sudden price changes) in the S&P 500 index. We will also create interactive charts and plots using Plotly Python and Seaborn for data visualization and display our results in Jupyter notebooks.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/ax5jib/anomaly
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores anomaly detection in time series data, which is standard in finance and other industries
Taught by Snehan Kekre, who is a recognized expert in time series analysis and anomaly detection
Develops skills in using Keras for deep learning and Plotly for data visualization, which are core skills for data scientists
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Reviews summary
Anomaly detection with deep learning
This course introduces anomaly detection in time series data with Keras. It covers building an anomaly detection model using deep learning, specifically designing and training an LSTM autoencoder using Keras API with Tensorflow 2 as the backend. The course also includes interactive charts and plots using Plotly Python and Seaborn for data visualization. Overall, learners found the course to be well-structured and informative, with a good balance of practical hands-on exercises and explanations. However, some learners expressed concerns about the limited time available for completing the project and the lack of explanation for certain coding steps.
Interactive charts and plots using Plotly Python and Seaborn
"The material did a good job of covering some useful katas for visualization using plotly and seaborn."
Introduces Keras API and Tensorflow 2 for deep learning
"It covers building an anomaly detection model using deep learning, specifically designing and training an LSTM autoencoder using Keras API with Tensorflow 2 as the backend."
Hands-on project with step-by-step guidance
"This course is very well structured and delivered."
"The instructor from rhyme was quite good. He explained every part, every function and reason behind their use quite clearly."
Limited time available for completing the project
"I was watching at 2x speed (an annotation told to use 1.5x speed) AND WAS STILL NOT FINISHED when the allotted time was over."
"Doesn't recommend it"
Insufficient explanation for certain coding steps
"The instructor doesn't explain why he is doing something, just does it."
"The instructor didn't explain why he does everything. He just did, and you just need to follow what he typed. After the project, you still didn't learn a lot."
Activities
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We're preparing activities for
Anomaly Detection in Time Series Data with Keras.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Anomaly Detection in Time Series Data with Keras will develop knowledge and skills
that may be useful to these careers:
Fraud Analyst
Fraud Analyst
Fraud Analysts investigate and prevent fraud and financial crimes. This course in Anomaly Detection in Time Series Data with Keras can help Fraud Analysts develop their skills in detecting anomalies in financial data, a critical skill in the field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course in Anomaly Detection in Time Series Data with Keras can help Quantitative Analysts develop their skills in detecting anomalies in financial data, a critical skill in the field.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course in Anomaly Detection in Time Series Data with Keras can help Statisticians develop their skills in using Keras to build LSTM autoencoders for anomaly detection, a critical technique in the field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course in Anomaly Detection in Time Series Data with Keras can help Software Engineers develop their skills in using Keras to build LSTM autoencoders for anomaly detection, a critical technique in the field.
Data Engineer
Data Engineer
Data Engineers design and maintain data pipelines and systems. This course in Anomaly Detection in Time Series Data with Keras can help Data Engineers develop their skills in using Keras to build LSTM autoencoders for anomaly detection, a critical technique in the field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. This course in Anomaly Detection in Time Series Data with Keras can help Machine Learning Engineers develop their skills in using Keras to build LSTM autoencoders for anomaly detection, a critical technique in the field.
Risk Manager
Risk Manager
Risk Managers identify, assess, and mitigate risks to an organization. This course in Anomaly Detection in Time Series Data with Keras can help Risk Managers develop their skills in detecting anomalies in financial data, a critical skill in the field.
Economist
Economist
Economists study the production, distribution, and consumption of goods and services. This course in Anomaly Detection in Time Series Data with Keras can help Economists develop their skills in using Keras to build LSTM autoencoders for anomaly detection, a critical technique in the field.
Compliance Analyst
Compliance Analyst
Compliance Analysts ensure that organizations comply with laws and regulations. This course in Anomaly Detection in Time Series Data with Keras can help Compliance Analysts develop their skills in detecting anomalies in financial data, a valuable skill in the field.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty in financial and insurance matters. This course in Anomaly Detection in Time Series Data with Keras can help Actuaries develop their skills in detecting anomalies in financial data, a valuable skill in the field.
Data Scientist
Data Scientist
Data Scientists combine programming skills with a deep understanding of statistics and machine learning to solve complex problems and make data-driven decisions. This course in Anomaly Detection in Time Series Data with Keras can help Data Scientists build a foundation in using Keras to detect anomalies in financial data, a valuable skill in the field.
Financial Analyst
Financial Analyst
Financial Analysts provide financial advice and guidance to individuals and businesses. This course in Anomaly Detection in Time Series Data with Keras can help Financial Analysts develop their skills in detecting anomalies in financial data, a valuable skill in the field.
Financial Planner
Financial Planner
Financial Planners provide financial advice and guidance to individuals and families. This course in Anomaly Detection in Time Series Data with Keras can help Financial Planners develop their skills in detecting anomalies in financial data, a valuable skill in the field.
Data Analyst
Data Analyst
Data Analysts use data to identify trends, patterns, and insights to support decision-making. This course in Anomaly Detection in Time Series Data with Keras can help Data Analysts develop their skills in detecting anomalies in financial data, a valuable skill in the field.
Business Analyst
Business Analyst
Business Analysts use data to improve business processes and make better decisions. This course in Anomaly Detection in Time Series Data with Keras can help Business Analysts develop their skills in detecting anomalies in financial data, a valuable skill in the field.
For more career information including salaries, visit:
OpenCourser.com/course/ax5jib/anomaly
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Anomaly Detection in Time Series Data with Keras.
A comprehensive and advanced textbook on time series analysis, covering advanced topics such as state-space models, Bayesian analysis, and nonlinear time series. Used as a textbook in graduate-level courses.
A comprehensive and advanced textbook on time series analysis using state-space methods. Covers theory, modeling, and forecasting techniques. Used as a textbook in graduate-level courses.
Covers the fundamentals and provides a detailed exposition of modeling time series. Discusses classical and modern methods, including ARMA, ARIMA, GARCH, and time-varying models. Serves as a reference and advanced textbook for researchers and practitioners in econometrics, statistics, finance, and other disciplines. Demonstrates methods using R and Python.
A classic textbook on time series analysis, covering both theoretical and practical aspects. Includes chapters on ARMA, ARIMA, seasonal models, and forecasting. Used as a textbook in many university courses.
Focuses on deep learning techniques for time series analysis, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and graph neural networks (GNNs). Covers model architectures, training algorithms, and evaluation metrics. A good choice for practitioners looking to apply deep learning to time series data.
Focuses on time series analysis and forecasting using the statistical programming language R. Covers time series analysis, forecasting techniques, and econometric modeling. Suitable for practitioners and researchers in economics, finance, and data science.
Save
A practical guide to time series analysis using R. Covers data exploration, model building, and forecasting. Includes examples and case studies from various disciplines.
Save
Provides a comprehensive overview of time series forecasting techniques. Covers both classical and advanced methods, including exponential smoothing, ARMA, ARIMA, and machine learning algorithms. Includes case studies and examples to illustrate practical applications.
Provides a practical introduction to time series analysis using Python. Covers data exploration, feature engineering, and model building. Includes hands-on examples and case studies to illustrate best practices.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ax5jib/anomaly
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Time
1 hours
Via
Coursera
Institutions
Rhyme
Coursera Project Network
Instructor
Snehan Kekre
Good to know
Explores anomaly detection in time series data, which is standard in finance and other industries
Taught by Snehan Kekre, who is a recognized expert in time series analysis and anomaly detection
Develops skills in using Keras for deep learning and Plotly for data visualization, which are core skills for data scientists
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