Course Summary
Learn practical time series analysis techniques for forecasting and anomaly detection. Explore real-world examples and apply concepts to your own data.Key Learning Points
- Gain hands-on experience with time series data
- Learn to use popular tools like ARIMA, Prophet, and LSTM networks
- Apply time series analysis for forecasting and anomaly detection
Job Positions & Salaries of people who have taken this course might have
- USA: $65,000 - $110,000
- India: ₹4,00,000 - ₹12,00,000
- Spain: €25,000 - €40,000
- USA: $65,000 - $110,000
- India: ₹4,00,000 - ₹12,00,000
- Spain: €25,000 - €40,000
- USA: $95,000 - $150,000
- India: ₹6,00,000 - ₹20,00,000
- Spain: €35,000 - €60,000
- USA: $65,000 - $110,000
- India: ₹4,00,000 - ₹12,00,000
- Spain: €25,000 - €40,000
- USA: $95,000 - $150,000
- India: ₹6,00,000 - ₹20,00,000
- Spain: €35,000 - €60,000
- USA: $70,000 - $120,000
- India: ₹4,50,000 - ₹15,00,000
- Spain: €28,000 - €45,000
Related Topics for further study
Learning Outcomes
- Understand the fundamentals of time series analysis
- Apply time series analysis techniques to real-world data
- Gain hands-on experience with popular time series analysis tools
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics and programming
- Familiarity with Python and data manipulation libraries like Pandas
Course Difficulty Level
IntermediateCourse Format
- Self-paced online course
- Video lectures
- Hands-on coding exercises
Similar Courses
- Applied Data Science: Time Series
- Time Series Forecasting
Related Education Paths
Notable People in This Field
- Professor of Statistics at Monash University
- Machine Learning Mastery
Related Books
Description
Welcome to Practical Time Series Analysis!
Outline
- WEEK 1: Basic Statistics
- Course Introduction
- Week 1 Welcome Video
- Getting Started in R: Download and Install R on Windows
- Getting Started in R: Download and Install R on Mac
- Getting Started in R: Using Packages
- Concatenation, Five-number summary, Standard Deviation
- Histogram in R
- Scatterplot in R
- Review of Basic Statistics I - Simple Linear Regression
- Reviewing Basic Statistics II More Linear Regression
- Reviewing Basic Statistics III - Inference
- Reviewing Basic Statistics IV
- Welcome to Week 1
- Getting Started with R
- Basic Statistics Review (with linear regression and hypothesis testing)
- Measuring Linear Association with the Correlation Function
- Visualization
- Basic Statistics Review
- Week 2: Visualizing Time Series, and Beginning to Model Time Series
- Week 2 Welcome Video
- Introduction
- Time plots
- First Intuitions on (Weak) Stationarity
- Autocovariance function
- Autocovariance coefficients
- Autocorrelation Function (ACF)
- Random Walk
- Introduction to Moving Average Processes
- Simulating MA(2) process
- All slides together for the next two lessons
- Noise Versus Signal
- Random Walk vs Purely Random Process
- Time plots, Stationarity, ACV, ACF, Random Walk and MA processes
- Week 3: Stationarity, MA(q) and AR(p) processes
- Week 3 Welcome Video
- Stationarity - Intuition and Definition
- Stationarity - First Examples...White Noise and Random Walks
- Stationarity - First Examples...ACF of Moving Average
- Series and Series Representation
- Backward shift operator
- Introduction to Invertibility
- Duality
- Mean Square Convergence (Optional)
- Autoregressive Processes - Definition, Simulation, and First Examples
- Autoregressive Processes - Backshift Operator and the ACF
- Difference equations
- Yule - Walker equations
- Stationarity - Examples -White Noise, Random Walks, and Moving Averages
- Stationarity - Intuition and Definition
- Stationarity - ACF of a Moving Average
- All slides together for lesson 2 and 4
- Autoregressive Processes- Definition and First Examples
- Autoregressive Processes - Backshift Operator and the ACF
- Yule - Walker equations - Slides
- Stationarity
- Series, Backward Shift Operator, Invertibility and Duality
- AR(p) and the ACF
- Difference equations and Yule-Walker equations
- Week 4: AR(p) processes, Yule-Walker equations, PACF
- Week 4 Welcome Video
- Partial Autocorrelation and the PACF First Examples
- Partial Autocorrelation and the PACF - Concept Development
- Yule-Walker Equations in Matrix Form
- Yule Walker Estimation - AR(2) Simulation
- Yule Walker Estimation - AR(3) Simulation
- Recruitment data - model fitting
- Johnson & Johnson-model fitting
- Partial Autocorrelation and the PACF First Examples
- Partial Autocorrelation and the PACF: Concept Development
- All slides together for the next two lessons
- Partial Autocorrelation
- Yule-Walker in matrix form and Yule-Walker estimation
- 'LakeHuron' dataset
- Week 5: Akaike Information Criterion (AIC), Mixed Models, Integrated Models
- Week 5 Welcome Video
- Akaike Information Criterion and Model Quality
- ARMA Models (And a Little Theory)
- ARMA Properties and Examples
- ARIMA Processes
- Q-Statistic
- Daily births in California in 1959
- Akaike Information Criterion and Model Quality
- ARMA Models and a Little Theory
- ARMA Properties and Examples
- All slides together for this lesson
- Daily birth dataset
- Daily female birth (R file)
- AIC and model building
- ARMA Processes
- ARIMA and Q-statistic
- 'BJsales' dataset
- Week 6: Seasonality, SARIMA, Forecasting
- Week 6 Welcome Video
- SARIMA processes
- ACF of SARIMA models
- SARIMA fitting: Johnson & Johnson
- SARIMA fitting: Milk production
- SARIMA fitting: Sales at a souvenir shop
- Forecasting Using Simple Exponential Smoothing
- Double Exponential Smoothing
- Triple Exponential Smoothing Concept Development
- Triple Exponential Smoothing Implementation
- All slides together for the next two lessons
- SARIMA simulation (code block)
- SARIMA code for J&J (code block)
- Forecasting using Simple Exponential Smoothing
- Forecasting Using Holt Winters for Trend (Double Exponential)
- Forecasting Using Holt Winters for Trend and Seasonality (Triple Exponential)
- SARIMA processes
- 'USAccDeaths' dataset
- Forecasting
Summary of User Reviews
Practical Time Series Analysis is a highly rated course on Coursera that provides a comprehensive introduction to time series analysis. Many users have praised the course for its practical approach and real-world examples, making it easy to understand and apply the concepts learned.Key Aspect Users Liked About This Course
The course is praised for its practical approach and use of real-world examples.Pros from User Reviews
- The course provides a strong foundation in time series analysis.
- The instructor is knowledgeable and explains concepts clearly.
- The course covers a wide range of topics and techniques.
- The assignments and quizzes are challenging but rewarding.
- The course is well-structured and easy to follow.
Cons from User Reviews
- The course can be challenging for those without a strong background in statistics.
- Some users have reported technical issues with the course platform.
- The course may not go into enough depth for advanced users.
- The course does not cover every aspect of time series analysis.
- Some users have found the pace of the course to be too slow or too fast.
Tural Sadigov
4.6 Raiting
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