Modeling Time Series and Sequential Data from Coursera

In this course you learn to build, refine, extrapolate, and, in some cases, interpret models designed for a single, sequential series. There are three modeling approaches presented. The traditional, Box-Jenkins approach for modeling time series is covered in the first part of the course. This presentation moves students from models for stationary data, or ARMA, to models for trend and seasonality, ARIMA, and concludes with information about specifying transfer function components in an ARIMAX, or time series regression, model. A Bayesian approach to modeling time series is considered next. The basic Bayesian framework is extended to accommodate autoregressive variation in the data as well as dynamic input variable effects. Machine learning algorithms for time series is the third approach. Gradient boosting and recurrent neural network algorithms are particularly well suited for accommodating nonlinear relationships in the data. Examples are provided to build intuition on the effective use of these algorithms.

The course concludes by considering how forecasting precision can be improved by combining the strengths of the different approaches. The final lesson includes demonstrations on creating combined (or ensemble) and hybrid model forecasts.

This course is appropriate for analysts interested in augmenting their machine learning skills with analysis tools that are appropriate for assaying, modifying, modeling, forecasting, and managing data that consist of variables that are collected over time.

This course uses a variety of different software tools. Familiarity with Base SAS, SAS/ETS, SAS/STAT, and SAS Visual Forecasting, as well as open-source tools for sequential data handling and modeling, is helpful but not required. The lessons on Bayesian analysis and machine learning models assume some prior knowledge of these topics. One way that students can acquire this background is by completing these SAS Education courses: Bayesian Analyses Using SAS and Machine Learning Using SAS Viya.

What's inside

Syllabus

Specialization Overview (Review)

In this module you get an overview of the courses in this specialization and what you can expect.

Course Overview

In this module, you get an idea of the scope of this course and learn to use SAS Viya for Learners to do the practices in the course.

Introduction to Time Series

This module reviews fundamental time series ideas. You learn about the basic components of systematic variation in time series data and some simple model specifications, such as the autoregressive order one and the random walk. You also learn about Exponential smoothing models or ESMs, selecting a champion ESM, and generating forecasts on time series.

ARIMAX Models

This module has four parts. The first part describes traditional models for stationary data: Auto Regressive Moving Average or ARMA models. The second part describes how the ARMA framework is generalized to accommodate trend variation. This involves integration, and results in the ARIMA model. The third part describes how the ARIMA model is adapted to handle seasonal variation in the data. The fourth and final part of the module introduces the dynamic regression or ARIMAX model and describes concepts related to identifying transfer function components and specifying ARIMAX models.

Bayesian Time Series Analysis

In this module, we combine the worlds of time series and Bayesian analysis. We begin with a brief review of Bayesian analysis. We then explore how to incorporate autoregressive, seasonal, and exogenous components in a Bayesian time series. We conclude with a discussion on Bayesian scoring and posterior predictive distributions.

Machine Learning Approaches to Time Series Modeling

In this module you learn how to use SAS machine learning tools to forecast individual time series. You learn to prepare the time series data for use with the machine learning tools, and how to build and score forecasting models using these tools. We focus on gradient boosting and recurrent neural network models and discuss when it would be useful to use these methods.

Hybrid Modeling Approaches and External Forecasts

This module describes how forecasts that are generated externally to the forecasting system can be accommodated in SAS Visual Forecasting. We'll use external forecasts to create a combined or ensemble forecast that has the potential to improve forecast precision relative to the constituent, external forecasts. This module concludes with a discussion of hybrid model forecasts that combine traditional and machine learning approaches to forecasting.

Course Review

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers modeling approaches like Box-Jenkins, Bayesian, and Machine Learning for time series analysis

Instructors Ari Zitin, Chip Wells, and Danny Modlin are recognized for their expertise in time series analysis

Builds a strong foundation in time series modeling techniques for beginners

Requires familiarity with Base SAS, SAS/ETS, SAS/STAT, and SAS Visual Forecasting, which may be a barrier for some students

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Modeling Time Series and Sequential Data with these activities:

Connect with Time Series Practitioners

Show steps

Seek guidance and insights from experienced professionals in the field of time series analysis.

Browse courses on Time Series Analysis

Show steps

Identify potential mentors through professional networking platforms or industry events.
Reach out to selected individuals and express your interest in mentorship.
Regularly engage with your mentor to discuss course-related topics and career advice.

Attend Time Series Webinars

Show steps

Connect with experts and stay updated on the latest developments in time series analysis.

Browse courses on Time Series

Show steps

Identify upcoming webinars hosted by industry professionals or academic institutions.
Register and attend the webinars to gain insights and learn about current trends.

Review Applied Time Series Analysis

Show steps

Review a popular book on time series analysis to enhance your understanding of time series concepts and models.

Show steps

Read the first three chapters to gain familiarity with the foundational concepts of time series analysis.
Focus on the chapters specific to the modeling techniques covered in the course, such as ARMA, ARIMA, and ARIMAX models.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Time Series Resources Collection

Show steps

Organize and expand your course resources to enhance understanding and facilitate future reference.

Browse courses on Time Series Analysis

Show steps

Gather relevant materials such as lecture notes, assignments, and external articles.
Categorize and compile the resources into a structured format, such as a digital or physical notebook.
Create an annotated bibliography to summarize key points and provide context.

Time Series Forecasting Drills

Show steps

Engage in hands-on practice through drills that reinforce key time series forecasting techniques.

Browse courses on Time Series Analysis

Show steps

Solve practice problems on data preprocessing, stationarity testing, model specification, and forecasting.
Utilize online resources or textbooks to supplement your practice.

Time Series Modeling Workshop

Show steps

Enhance your practical skills through a structured workshop focused on time series modeling techniques.

Browse courses on Time Series Analysis

Show steps

Enroll in a workshop led by experienced practitioners in the field.
Actively participate in the hands-on exercises and discussions.
Leverage the opportunity to clarify concepts and ask questions to experts.

Machine Learning Tutorials for Time Series

Show steps

Seek out and follow in-depth tutorials that cover the integration of machine learning techniques in time series analysis.

Browse courses on Time Series Analysis

Show steps

Identify and enroll in online tutorials that focus on machine learning algorithms for time series forecasting.
Complete the tutorials at your own pace, actively practicing the concepts and techniques.
Experiment with the machine learning algorithms on real-time series datasets.

Time Series Forecasting Project

Show steps

Apply your knowledge by developing a forecasting model for a real-world time series dataset.

Browse courses on Time Series Analysis

Show steps

Select a dataset that aligns with your interests and the course content.
Preprocess the data, evaluate stationarity, and determine appropriate model specifications.
Develop and evaluate multiple time series models using the techniques learned in the course.
Present your results and discuss the insights gained from the forecasting exercise.

Career center

Learners who complete Modeling Time Series and Sequential Data will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist combines programming skills, statistical methods, and machine learning techniques to extract insights from data. They work in various industries, including technology, finance, healthcare, and manufacturing. The course in Modeling Time Series and Sequential Data can be beneficial for aspiring Data Scientists, as it provides a strong foundation in time series analysis, machine learning algorithms, and forecasting techniques, which are essential for handling time-series data and making predictions.

See salaries and explore the career path for Data Scientist

Quantitative Analyst

A Quantitative Analyst (Quant) applies mathematical and statistical methods to analyze financial data and develop trading strategies in the financial markets. The course in Modeling Time Series and Sequential Data can be beneficial for those interested in a career as a Quant, as it provides a strong foundation in time series analysis, statistical modeling, and forecasting techniques, which are essential for analyzing financial data and making informed trading decisions.

See salaries and explore the career path for Quantitative Analyst

Biostatistician

A Biostatistician utilizes statistical methods to answer research questions in the fields of biology and medicine. With the rise of personalized and precision medicine, Biostatisticians play an increasingly important role in interpreting data and informing clinical decisions. This course in Modeling Time Series and Sequential Data can be a valuable addition to the skill set of a Biostatistician, as it provides a solid foundation in time series analysis, which is essential for analyzing longitudinal data and making predictions in the healthcare domain.

See salaries and explore the career path for Biostatistician

Data Analyst

A Data Analyst collects, analyzes, and interprets data to extract insights and inform decision-making. The course in Modeling Time Series and Sequential Data can be useful for those interested in a career as a Data Analyst, as it provides a solid foundation in time series analysis, statistical modeling, and forecasting techniques, which are essential for analyzing time-series data and making predictions.

See salaries and explore the career path for Data Analyst

Climate Scientist

A Climate Scientist studies the Earth's climate system and its long-term changes. The course in Modeling Time Series and Sequential Data can be useful for those interested in a career as a Climate Scientist, as it provides a strong foundation in time series analysis and forecasting techniques, which are essential for analyzing climate data and making predictions about future climate trends.

See salaries and explore the career path for Climate Scientist

Machine Learning Engineer

A Machine Learning Engineer builds, deploys, and maintains machine learning models to solve real-world problems. They collaborate with data scientists and software engineers to develop and implement machine learning solutions. The course in Modeling Time Series and Sequential Data can be helpful for those interested in a career as a Machine Learning Engineer, as it provides a solid understanding of time series analysis, machine learning algorithms, and forecasting techniques, which are essential for developing and deploying machine learning models for time-series data.

See salaries and explore the career path for Machine Learning Engineer

Statistician

A Statistician collects, analyzes, and interprets data to draw conclusions and make predictions. The course in Modeling Time Series and Sequential Data can be useful for those interested in a career as a Statistician, as it provides a solid foundation in time series analysis and forecasting techniques, which are essential for analyzing time-series data and making predictions.

See salaries and explore the career path for Statistician

Epidemiologist

An Epidemiologist investigates the causes and patterns of disease outbreaks and develops strategies to prevent and control them. The course in Modeling Time Series and Sequential Data can be useful for those interested in a career as an Epidemiologist, as it provides a strong foundation in time series analysis and forecasting techniques, which are essential for analyzing disease surveillance data and making predictions about future outbreaks.

See salaries and explore the career path for Epidemiologist

Operations Research Analyst

An Operations Research Analyst uses mathematical and analytical techniques to solve complex problems in various industries, including manufacturing, transportation, and healthcare. The course in Modeling Time Series and Sequential Data can be useful for those interested in a career as an Operations Research Analyst, as it provides a solid foundation in time series analysis, optimization techniques, and forecasting methods, which are essential for analyzing operational data and making informed decisions.

See salaries and explore the career path for Operations Research Analyst

Actuary

An Actuary analyzes and manages risks in the insurance and financial services industries. The course in Modeling Time Series and Sequential Data can be beneficial for those interested in a career as an Actuary, as it provides a solid foundation in time series analysis, statistical modeling, and forecasting techniques, which are essential for analyzing insurance data and making informed risk assessments.

See salaries and explore the career path for Actuary

Business Analyst

A Business Analyst analyzes business processes and data to improve efficiency and productivity. The course in Modeling Time Series and Sequential Data can be useful for those interested in a career as a Business Analyst, as it provides a solid foundation in time series analysis and forecasting techniques, which are essential for analyzing business data and making informed decisions.

See salaries and explore the career path for Business Analyst

Financial Analyst

A Financial Analyst provides guidance to individuals and organizations on investment decisions. They analyze financial data and economic trends to develop investment strategies, manage portfolios, and make recommendations to clients. The course in Modeling Time Series and Sequential Data can be useful for those interested in a career as a Financial Analyst, as it provides a thorough understanding of time series analysis and forecasting techniques, which are essential for analyzing financial data and making informed investment decisions.

See salaries and explore the career path for Financial Analyst

Market Researcher

A Market Researcher conducts research to understand consumer behavior and market trends. The course in Modeling Time Series and Sequential Data can be useful for those interested in a career as a Market Researcher, as it provides a solid foundation in time series analysis and forecasting techniques, which are essential for analyzing market data and making predictions about future market trends.

See salaries and explore the career path for Market Researcher

Environmental Scientist

An Environmental Scientist studies the environment and its interactions with humans. The course in Modeling Time Series and Sequential Data can be useful for those interested in a career as an Environmental Scientist, as it provides a solid foundation in time series analysis and forecasting techniques, which are essential for analyzing environmental data and making predictions about future environmental trends.

See salaries and explore the career path for Environmental Scientist

Software Engineer

A Software Engineer designs, develops, and maintains software systems. The course in Modeling Time Series and Sequential Data may be useful for those interested in a career as a Software Engineer, as it provides a solid foundation in time series analysis and forecasting techniques, which may be needed in specialized software engineering roles such as developing predictive analytics systems or data visualization tools.

See salaries and explore the career path for Software Engineer