Demand Forecasting Using Time Series from Coursera

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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.

Independence and Autocorrelation

In this module, we'll dive into the ideas behind autocorrelation and independence. We'll start by digging into the math of correlation and how it can be used to characterize the relationship between two variables. Next, we'll define its relationship to independence and explain where these ideas can be used. Finally, we'll combine correlation with time series attributes, such as trend, seasonality, and stationarity to derive autocorrelation. We'll go through both some of the theory behind autocorrelation, and how to code it in Python.

Regression and ARIMA Models

In this module, we'll start by reviewing some of the basic concepts behind linear regression. Then, we'll extend this knowledge to feed into lagged regression, an effective way to use regression techniques on time series. Once we have a solid foothold in basic and lagged regression, we'll explore modern methods such as ARIMA (autoregressive integrated moving average). All of this is building the framework for more advanced machine learning models such as LSTMs (long short-term memory network).

Final Project

In the final course project, we'll make demand predictions using ARIMA models.

Good to know

Know what's good

, what to watch for

, 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

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

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Reinforce your prior knowledge of time series concepts to enhance your understanding of the course material.

Browse courses on Time Series

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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

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Enhance your understanding of time series patterns by creating visualizations.

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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

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Supplement your understanding of autocorrelation and its significance in time series analysis.

Browse courses on Autocorrelation

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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

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Regression and ARIMA Modeling

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Gain proficiency in regression and ARIMA modeling techniques for time series analysis.

Browse courses on Linear Regression

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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

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Connect with professionals in the field to gain insights and expand your knowledge.

Browse courses on Supply Chain Management

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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

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Apply your knowledge of time series analysis by building a demand forecasting model using ARIMA techniques.

Browse courses on Time Series Forecasting

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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

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Deepen your understanding of time series techniques by contributing to open-source projects.

Browse courses on Time Series Analysis

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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.

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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.

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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.

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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.

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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.

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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.

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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

Demand Forecasting Using Time Series

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