Practical Time Series Analysis

Go to Course: https://www.coursera.org/learn/practical-time-series-analysis

Introduction

## Course Review: Practical Time Series Analysis **Overview** The course "Practical Time Series Analysis" on Coursera is a meticulously designed program aimed at professionals who find themselves dealing with data analytics in their respective fields without formal training. Whether you hail from sciences, engineering, or business, this course caters to those who desire a more in-depth understanding of time series analysis without resorting to a mere "cookbook" methodology. With a strong emphasis on both theory and practical application, this course is excellent for individuals looking to enhance their skills in analyzing time-dependent data. ### Course Structure and Syllabus The six-week curriculum is structured to progressively build your knowledge and skills in time series analysis, culminating in practical applications of forecasting techniques. Here’s a week-by-week breakdown: **Week 1: Basic Statistics** The course kicks off with a foundational week that covers the necessary basics of inferential and descriptive statistics, ensuring that participants have a solid base to draw upon. The practical aspect is highlighted through instructions on installing R, setting the stage for hands-on learning. **Week 2: Visualizing Time Series and Beginning to Model Time Series** In the second week, learners delve into time series visualization using datasets. This week is crucial as it introduces mathematical modeling techniques—an essential part of time series analysis—building on the knowledge gained in the previous week. **Week 3: Stationarity, MA(q) and AR(p) Processes** Week three dives into more complex concepts crucial for time series analysis, such as stationarity and autoregressive processes. The introduction of Yule-Walker equations provides a theoretical underpinning that enhances understanding. **Week 4: AR(p) Processes, Yule-Walker Equations, PACF** Building on previous weeks, this segment emphasizes Partial Autocorrelation (PACF). This week intricately intertwines theory and real-world application, solidifying learners’ understanding through practical datasets. **Week 5: Akaike Information Criterion (AIC), Mixed Models, Integrated Models** In the penultimate week, the course introduces Akaike Information Criterion (AIC) for model selection. Participants get hands-on experience with mixed models like ARMA and ARIMA, which are pivotal for accurately analyzing various datasets. **Week 6: Seasonality, SARIMA, Forecasting** The final week is where theoretical concepts come together in practice. Participants learn about seasonality and apply SARIMA models to fit and forecast different datasets. This is the culmination of the entire course, empowering students with the skills to predict future trends based on historical data. ### Course Experience Throughout the course, the combination of video lectures, hands-on practice, and theoretical explanations creates a well-rounded educational experience. The use of R for practical applications is a significant advantage, as it is one of the most widely used software environments among data analysts. The course leverages real-world datasets, which enhances engagement and ensures that lessons are applicable beyond the classroom. **Community and Support** Discussion forums provide an opportunity to engage with fellow learners, allowing for the exchange of ideas and solutions to challenges encountered during the course. The instructors are also available for guidance, adding a personalized touch to the learning experience. ### Recommendation I highly recommend "Practical Time Series Analysis" for anyone looking to deepen their understanding of time series data analysis. This course is particularly beneficial for professionals who are already familiar with basic data analysis concepts but need to develop a more comprehensive and practical skill set in time series analysis. Whether you are aiming to enhance your career prospects, conduct more effective research, or simply satisfy a personal interest in data analytics, this course will equip you with the essential tools and techniques needed to confidently tackle time series analysis. Sign up now to take your analytical skills to the next level!

Syllabus

WEEK 1: Basic Statistics

During this first week, we show how to download and install R on Windows and the Mac. We review those basics of inferential and descriptive statistics that you'll need during the course.

In this week, we begin to explore and visualize time series available as acquired data sets. We also take our first steps on developing the mathematical models needed to analyze time series data.

In Week 3, we introduce few important notions in time series analysis: Stationarity, Backward shift operator, Invertibility, and Duality. We begin to explore Autoregressive processes and Yule-Walker equations.

In this week, partial autocorrelation is introduced. We work more on Yule-Walker equations, and apply what we have learned so far to few real-world datasets.

In Week 5, we start working with Akaike Information criterion as a tool to judge our models, introduce mixed models such as ARMA, ARIMA and model few real-world datasets.

In the last week of our course, another model is introduced: SARIMA. We fit SARIMA models to various datasets and start forecasting.