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Practical Time Series Analysis

Approx. 26 hours to complete

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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

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

Intermediate

Course 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!

Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future. Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn. You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself! Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!

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.

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