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Demand Forecasting Using Time Series

Rajvir Dua and Neelesh Tiruviluamala

This course is the second in a specialization for Machine Learning for Supply Chain Fundamentals. In this course, we explore all aspects of time series, especially for demand prediction. We'll start by gaining a foothold in the basic concepts surrounding time series, including stationarity, trend (drift), cyclicality, and seasonality. Then, we'll spend some time analyzing correlation methods in relation to time series (autocorrelation). In the 2nd half of the course, we'll focus on methods for demand prediction using time series, such as autoregressive models. Finally, we'll conclude with a project, predicting demand using ARIMA models in Python.

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What's inside

Syllabus

A First Glance at Time Series
In this module, we'll get our feet wet with time series in Python. We'll start by getting familiar with where time series fits in to the machine learning landscape. Then, we'll learn about the main types of time series and their distinguishing factors, including period, frequency, and stationarity. After pausing to learn how to plot timeseries in Python, we'll explore the differences between seasonality and cyclicality.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches methods and models for analyzing time series, which is standard in industry
Taught by seasoned instructors recognized for their expertise in machine learning and supply chain
Covers time series prediction, a technique that is highly sought after in industry
Involves a practical project that allows learners to apply the concepts

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Reviews summary

Practical demand forecasting with time series in python

According to students, this course on Demand Forecasting Using Time Series is a highly beneficial program, widely praised for its practical application and hands-on Python labs. Learners found the instructor's explanations clear, effectively breaking down complex topics like ARIMA models. The final project is highlighted as crucial for solidifying understanding and applying skills to real-world demand prediction. While offering a strong foundation, some students note the fast pace, suggesting prior knowledge in Python and statistics is advantageous for optimal learning. It's considered highly relevant for professionals in supply chain and data analysis.
Strong coverage of ARIMA and foundational time series concepts.
"The ARIMA section was comprehensive enough for a beginner."
"The course does a great job of explaining autocorrelation and how it relates to time series."
"This course provided a fantastic foundation in time series for demand prediction."
"The flow from basic concepts to advanced models was smooth."
Instructor clarity and well-organized course structure.
"The instructor explained complex concepts very well, making them accessible."
"Very well-structured and easy to follow. The instructor's explanations of ARIMA and other time series concepts were top-notch."
"I found the balance between theory and practical coding to be just right."
"The course broke down complex topics into digestible modules."
Focuses on real-world utility and Python implementation.
"This course was exactly what I needed to bridge the gap between theoretical knowledge and practical application in demand forecasting."
"The hands-on approach with Python was incredibly effective. I've been looking for a course that truly focuses on the practical side of demand prediction..."
"As someone working in supply chain, this is directly applicable to my job."
"I learned how to apply time series analysis to real-world demand forecasting problems."
Course pace benefits learners with background in Python/statistics.
"My only minor critique is that some parts felt a bit rushed, especially if you're completely new to Python or statistics."
"I struggled with this course. I came in with very little background in Python or statistics, and it quickly ramped up."
"I'd recommend having some prior exposure to data science concepts, as it moves quite quickly."
"Learners should have at least an intermediate grasp of Python and basic statistics to get the most out of it..."

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 Demand Forecasting Using Time Series with these activities:
Review Time Series Concepts
Reinforce your prior knowledge of time series concepts to enhance your understanding of the course material.
Browse courses on Time Series
Show steps
  • Revisit fundamental concepts such as stationarity, trend, seasonality, and cyclicality.
  • Review the different types of time series data and their characteristics.
  • Practice identifying and visualizing trends, seasonality, and cyclicality in time series data.
Visualize Time Series Data
Enhance your understanding of time series patterns by creating visualizations.
Show steps
  • Choose appropriate visualization techniques for time series data.
  • Create visualizations of time series data to identify trends, seasonality, and anomalies.
  • Interpret the visualizations and draw insights from the time series data.
Autocorrelation and Time Series Analysis
Supplement your understanding of autocorrelation and its significance in time series analysis.
Browse courses on Autocorrelation
Show steps
  • Follow tutorials on autocorrelation and its calculation methods.
  • Explore different techniques for analyzing autocorrelation in time series data.
  • Practice interpreting autocorrelation plots to identify patterns and trends.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Regression and ARIMA Modeling
Gain proficiency in regression and ARIMA modeling techniques for time series analysis.
Browse courses on Linear Regression
Show steps
  • Solve practice problems involving linear regression models for time series data.
  • Implement ARIMA models in Python to make demand predictions.
  • Interpret the results of ARIMA models and evaluate their performance.
Attend Industry Conferences
Connect with professionals in the field to gain insights and expand your knowledge.
Browse courses on Supply Chain Management
Show steps
  • Identify relevant industry conferences and events.
  • Attend sessions and workshops on time series analysis and demand forecasting.
  • Network with industry experts and practitioners to exchange ideas and learn about emerging trends.
Time Series Forecasting Project
Apply your knowledge of time series analysis by building a demand forecasting model using ARIMA techniques.
Browse courses on Time Series Forecasting
Show steps
  • Gather and prepare time series data for demand forecasting.
  • Apply ARIMA modeling to the time series data to make predictions.
  • Evaluate the performance of your ARIMA model and present your findings in a report.
Contribute to Time Series Analysis Libraries
Deepen your understanding of time series techniques by contributing to open-source projects.
Browse courses on Time Series Analysis
Show steps
  • Explore open-source libraries for time series analysis in Python.
  • Identify areas where you can make meaningful contributions based on your knowledge gained in this course.
  • Submit pull requests with code contributions, bug fixes, or documentation improvements.

Career center

Learners who complete Demand Forecasting Using Time Series will develop knowledge and skills that may be useful to these careers:
Quantitative Analyst
Quantitative analysts, or "quants," use mathematical and statistical modeling, such as time series analysis, to make predictions about financial markets. This course can help build a foundation for a career as a quantitative analyst by providing a solid understanding of time series analysis, autocorrelation, and forecasting methods. The course's focus on demand forecasting in particular can be especially beneficial for quants working in areas such as financial forecasting and risk management.
See salaries and explore the career path for Quantitative Analyst
Data Scientist
Data scientists use their expertise in statistics, programming, and machine learning to extract insights from data. Time series analysis is a key skill for data scientists, and this course provides a comprehensive overview of the topic. The course's emphasis on demand forecasting is also relevant for data scientists working in areas such as retail, manufacturing, and supply chain management.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine learning engineers build, deploy, and maintain machine learning models. Time series analysis is a common task for machine learning engineers, and this course provides a solid foundation in the topic. The course's focus on demand forecasting is also relevant for machine learning engineers working in areas such as predictive maintenance and fraud detection.
See salaries and explore the career path for Machine Learning Engineer
Business Analyst
Business analysts use data to help businesses make better decisions. Time series analysis is a valuable skill for business analysts, as it can be used to forecast demand, identify trends, and make other predictions. This course provides a comprehensive overview of time series analysis, and it is especially relevant for business analysts working in areas such as marketing, finance, and operations.
See salaries and explore the career path for Business Analyst
Statistician
Statisticians use statistical methods to collect, analyze, and interpret data. Time series analysis is a specialized area of statistics that is used to analyze data that is collected over time. This course provides a comprehensive overview of time series analysis, and it is especially relevant for statisticians working in areas such as forecasting, risk management, and quality control.
See salaries and explore the career path for Statistician
Financial Analyst
Financial analysts use financial data to make investment recommendations. Time series analysis is a valuable skill for financial analysts, as it can be used to forecast financial performance, identify trends, and make other predictions. This course provides a comprehensive overview of time series analysis, and it is especially relevant for financial analysts working in areas such as portfolio management, equity research, and credit analysis.
See salaries and explore the career path for Financial Analyst
Market Researcher
Market researchers use research methods to collect and analyze data about markets. Time series analysis is a valuable skill for market researchers, as it can be used to forecast demand, identify trends, and make other predictions. This course provides a comprehensive overview of time series analysis, and it is especially relevant for market researchers working in areas such as consumer behavior, product development, and marketing strategy.
See salaries and explore the career path for Market Researcher
Operations Research Analyst
Operations research analysts use mathematical and analytical methods to solve problems in business and industry. Time series analysis is a valuable skill for operations research analysts, as it can be used to forecast demand, optimize inventory levels, and make other predictions. This course provides a comprehensive overview of time series analysis, and it is especially relevant for operations research analysts working in areas such as supply chain management, logistics, and manufacturing.
See salaries and explore the career path for Operations Research Analyst
Economist
Economists use economic theory and data to analyze and forecast economic trends. Time series analysis is a valuable skill for economists, as it can be used to forecast economic growth, inflation, and other economic indicators. This course provides a comprehensive overview of time series analysis, and it is especially relevant for economists working in areas such as monetary policy, fiscal policy, and international economics.
See salaries and explore the career path for Economist
Software Engineer
Software engineers design, develop, and maintain software systems. While not a direct fit for the course, the concepts of time series analysis and autocorrelation can be applied to a variety of software engineering problems, such as performance monitoring, predictive maintenance, and anomaly detection.
See salaries and explore the career path for Software Engineer
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. While not a direct fit for the course, time series analysis can be used to model and forecast a variety of risks, such as insurance claims, mortality rates, and financial market volatility.
See salaries and explore the career path for Actuary
Data Analyst
Data analysts use data to solve business problems. While not a direct fit for the course, time series analysis can be used to identify trends, forecast demand, and make other predictions from data.
See salaries and explore the career path for Data Analyst
Marketing Manager
Marketing managers develop and execute marketing campaigns. While not a direct fit for the course, time series analysis can be used to forecast demand, track marketing campaign performance, and make other predictions.
See salaries and explore the career path for Marketing Manager
Sales Manager
Sales managers lead and motivate sales teams. While not a direct fit for the course, time series analysis can be used to forecast sales, identify trends, and make other predictions.
See salaries and explore the career path for Sales Manager
Financial Advisor
Financial advisors provide financial advice to individuals and families. While not a direct fit for the course, time series analysis can be used to forecast investment performance and make other predictions.
See salaries and explore the career path for Financial Advisor

Reading list

We've selected six 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 Demand Forecasting Using Time Series.
Cover image
Time Series Analysis
Save
Classic in the field of time series analysis and forecasting. It provides a comprehensive overview of the Box-Jenkins approach, which widely used method for forecasting time series data.
Time Series Analysis: Forecasting and Control...
Hardcover
$$$$
Time Series Analysis: Forecasting and Control...
Kindle Edition
$$$$
Cover image
Forecasting
Save
Provides a comprehensive overview of forecasting methods, including both traditional and modern approaches. It valuable resource for anyone who wants to learn more about forecasting.
Forecasting: Principles and Practice
Paperback
$$$
Forecasting: principles and practice
Paperback
$$$
Forecasting: principles and practice
Paperback
$$$
Cover image
Time Series Analysis and Its Applications
Save
Provides a comprehensive overview of time series analysis and forecasting. It includes a number of R examples, which makes it a valuable resource for anyone who wants to learn more about time series analysis in R.
Time Series Analysis and Its Applications: With R...
Paperback
$$$
Time Series Analysis and Its Applications: With R...
Hardcover
$$$
Time Series Analysis and Its Applications (Springer...
Kindle Edition
$$$
Cover image
Time Series Analysis
Save
Provides a comprehensive overview of time series analysis, including both classical and modern methods. It valuable reference for anyone interested in forecasting and control.
Time Series Analysis: Forecasting and Control...
Hardcover
$$$$
Time Series Analysis: Forecasting and Control...
Kindle Edition
$$$$
Cover image
Structural Vector Autoregressive Analysis
Save
Provides a comprehensive overview of time series analysis and forecasting methods, with a focus on applications in economics. It valuable resource for anyone who wants to learn more about how to use time series analysis to solve problems in economics.
Structural Vector Autoregressive Analysis (Themes...
Paperback
$$$
Structural Vector Autoregressive Analysis (Themes...
Kindle Edition
$$$
Cover image
Time Series Analysis
Save
Provides a comprehensive introduction to time series econometrics. It is written in a clear and concise style, and it is packed with examples and exercises.
Time Series Analysis
Hardcover
$$$
Time Series Analysis
Kindle Edition
$$$$

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Course image
Effort
4 hours of study, ~ 2 hours/week
Level
Intermediate
Via
Coursera
Institution
LearnQuest
Instructors
Rajvir Dua
Neelesh Tiruviluamala
Language
English
Transcripts
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This course belongs to a collection called Machine Learning for Supply Chains. It's comprised of four courses:
  1. Advanced AI Techniques for the Supply Chain
  2. Capstone Project: Predicting Safety Stock
  3. Demand Forecasting Using Time Series (viewing)
  4. Fundamentals of Machine Learning for Supply Chain
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Teaches methods and models for analyzing time series, which is standard in industry
Taught by seasoned instructors recognized for their expertise in machine learning and supply chain
Covers time series prediction, a technique that is highly sought after in industry
Involves a practical project that allows learners to apply the concepts
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