Excel Time Series Models for Business Forecasting from Coursera

What's inside

Syllabus

Welcome and Critical Information

Business Forecasting is part of any and every organisation. Organisations need to forecast so that they can plan for the organisation’s needs. Business forecasts are the inputs to every organisation’s planning – without business forecasts we cannot plan for our resources, our production, our supply chains – and ultimately our costs, revenues and profits. The current state of the world makes business forecasting even more fundamental to the operation of institutions. In this course we focus on Excel Skills for Business Forecasting using Time Series Models. We will be looking at how your business can utilise time series data sets to understand the different components underlying this data, and then apply the relevant model depending on these components. We will look at a range of business forecasting methods, and sometimes, more than one method may be needed! The models we look at are: Naïve Forecasting, Moving Averages, Trend-fitting, Simple Exponential Smoothing, Holt’s Exponential Smoothing, Winters Exponential Smoothing, and Decomposition. This course then continues in our second course in this specialisation which looks at Regression Models, and our third course in this specialisation which looks at Judgmental Forecasting. #EveryoneSayWow

Time Series Models

In this module, we explore the context and purpose of business forecasting and the three types of business forecasting — time series, regression, and judgmental. This course focuses on time series models. We will learn about time series models, as well as the component of time series data. We will then look at a preliminary forecasting method — Average Forecasts. Once we have a forecast, we need a tool to judge the accuracy of the forecasts — which are the forecasts and the error criterion calculated from these.

Level Time Series

In this module, we explore different time series forecasting methods available for data that is level.

Trending Time Series

In this module, we explore different time series forecasting methods available for data that is trending.

Seasonal Time Series

In this module, we explore a time series forecasting method (Winters Exponential Smoothing) available for data that is seasonal.

Decomposition

In this module, we explore a time series forecasting method (Decomposition) available for data that is seasonal.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops mastery of data analysis and forecasting tools, such as Excel

Suitable if learners have an intermediate working knowledge of Excel

Designed for learners with an interest in business forecasting

Covers fundamental elements of time series forecasting

Provides hands-on practice in applying forecasting methods

Involves assessments such as modeling and forecasting exercises

Reviews summary

Excel time series forecasting

According to students, this highly recommended course deftly explains advanced excel capabilities used in forecasting. The engaging course covers a wide array of topics, including time series analysis, exponential smoothing, and ARIMA models. Students describe the instructor's teaching style as easy to follow and note that he does an excellent job presenting complex concepts.

Applicable to Real-World Scenarios

"The content is very concrete and practical for real case scenarios."

"I can take this knowledge and use it immediately."

Beginner Friendly

"Beautifully designed for beginners."

"Amazing content and well prepared lessons."

Highly Informative

"wonderful course and I learned a lot. I can definitely say WOW!"

"Excellent Content"

Includes Practical Exercises

"Great course with great hands-on"

"Very straightforward teching. Excellent material to work with"

Highly Knowledgeable

"Excellent tutor. Clear concise and great examples to work along to. Thank you"

"The course content is just superb, and the way the faculty is teaching the course is just fantastic. "

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 Excel Time Series Models for Business Forecasting with these activities:

Read 'Time Series Analysis and Forecasting by Shumway and Stoffer'

Show steps

Enhance understanding of time series analysis and forecasting concepts through a thorough review of a comprehensive textbook.

View Time Series Analysis and Its Applications: With... on Amazon

Show steps

Obtain a copy of the book
Read and study the relevant chapters
Complete the exercises and problems in the book

Review time series data basics

Show steps

Improve recall of time series data basics such as components, trends, and seasonality.

Browse courses on Time Series Data

Show steps

Review notes or textbooks on time series data
Complete practice problems on time series data analysis

Engage in peer discussions on time series forecasting

Show steps

Foster collaboration and knowledge exchange by engaging in discussions with peers.

Browse courses on Time Series Forecasting

Show steps

Join online forums or study groups related to time series forecasting
Participate in discussions, sharing insights and asking questions

Four other activities

Expand to see all activities and additional details

Show all seven activities

Solve time series forecasting problems

Show steps

Strengthen problem-solving skills in time series forecasting through practice.

Browse courses on Time Series Analysis

Show steps

Find online resources or textbooks with time series forecasting problems
Attempt to solve the problems independently
Check solutions and identify areas for improvement

Attend a workshop on time series forecasting best practices

Show steps

Gain insights from industry experts and learn advanced techniques in time series forecasting.

Browse courses on Time Series Analysis

Show steps

Search for upcoming workshops on time series forecasting
Register for and attend the workshop
Actively participate and take notes

Develop a time series forecasting model for a real-world dataset

Show steps

Apply course learnings to a practical scenario, deepening understanding and honing implementation skills.

Browse courses on Time Series Analysis

Show steps

Identify a real-world dataset with time series data
Choose appropriate time series forecasting methods based on data analysis
Develop the forecasting model and evaluate its performance
Document the process and present the findings

Contribute to open-source time series forecasting projects

Show steps

Gain practical experience and contribute to the community by participating in open-source time series forecasting projects.

Browse courses on Time Series Forecasting

Show steps

Identify open-source time series forecasting projects on platforms like GitHub
Review the project documentation and identify areas for contribution
Submit pull requests with your contributions

Career center

Learners who complete Excel Time Series Models for Business Forecasting will develop knowledge and skills that may be useful to these careers:

Time Series Analyst

Time Series Analysts are responsible for analyzing time series data to identify trends and patterns. This course may be useful in providing Time Series Analysts with a foundation in time series forecasting methods, which can be used to analyze historical data and make predictions about future trends. This can be valuable in developing and implementing time series models.

See salaries and explore the career path for Time Series Analyst

Data Scientist

Data Scientists are responsible for developing and using mathematical and statistical models to analyze data. This course may be useful in providing Data Scientists with a foundation in time series forecasting methods, which can be used to analyze historical data and make predictions about future trends. This can be valuable in developing and implementing data-driven solutions.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians are responsible for collecting, analyzing, and interpreting data. This course may be useful in providing Statisticians with a foundation in time series forecasting methods, which can be used to analyze historical data and make predictions about future trends. This can be valuable in developing and implementing statistical models.

See salaries and explore the career path for Statistician

Actuary

Actuaries are responsible for assessing and managing financial risks. This course may be useful in providing Actuaries with a foundation in time series forecasting methods, which can be used to analyze historical financial data and make predictions about future trends. This can be valuable in developing and implementing actuarial models.

See salaries and explore the career path for Actuary

Quantitative Analyst

Quantitative Analysts are responsible for developing and using mathematical and statistical models to analyze financial data. This course may be useful in providing Quantitative Analysts with a foundation in time series forecasting methods, which can be used to analyze historical financial data and make predictions about future trends. This can be valuable in making informed investment decisions.

See salaries and explore the career path for Quantitative Analyst

Investment Analyst

Investment Analysts are responsible for analyzing financial data and making recommendations on investments. This course may be useful in providing Investment Analysts with a foundation in time series forecasting methods, which can be used to analyze historical financial data and make predictions about future trends. This can be valuable in making informed investment decisions.

See salaries and explore the career path for Investment Analyst

Operations Research Analyst

Operations Research Analysts are responsible for using mathematical and statistical models to improve the efficiency and effectiveness of business operations. This course may be useful in providing Operations Research Analysts with a foundation in time series forecasting methods, which can be used to analyze historical data and make predictions about future trends. This can be valuable in developing and implementing operational improvements.

See salaries and explore the career path for Operations Research Analyst

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course may be useful in providing Data Analysts with a foundation in time series forecasting methods, which can be used to analyze historical data and make predictions about future trends. This can be valuable in developing data-driven insights and making informed decisions.

See salaries and explore the career path for Data Analyst

Trading Analyst

Trading Analysts are responsible for analyzing financial data to identify trading opportunities. This course may be useful in providing Trading Analysts with a foundation in time series forecasting methods, which can be used to analyze historical financial data and make predictions about future trends. This can be valuable in making informed trading decisions.

See salaries and explore the career path for Trading Analyst

Risk Analyst

Risk Analysts are responsible for identifying, assessing, and mitigating risks. This course may be useful in providing Risk Analysts with a foundation in time series forecasting methods, which can be used to analyze historical data and make predictions about future risks. This can be valuable in developing and implementing risk management strategies.

See salaries and explore the career path for Risk Analyst

Market Researcher

Market Researchers are responsible for collecting and analyzing data on consumer behavior and trends. This course may be useful in providing Market Researchers with a foundation in time series forecasting methods, which can be used to analyze historical market data and make predictions about future trends. This can be valuable in developing effective marketing strategies.

See salaries and explore the career path for Market Researcher

Financial Analyst

Financial Analysts are responsible for analyzing financial data and making recommendations on investments. This course may be useful in providing Financial Analysts with a foundation in time series forecasting methods, which can be used to analyze historical financial data and make predictions about future trends. This can be valuable in making informed investment decisions.

See salaries and explore the career path for Financial Analyst

Sales Analyst

Sales Analysts are responsible for collecting and analyzing data on sales performance. This course may be useful in providing Sales Analysts with a foundation in time series forecasting methods, which can be used to analyze historical sales data and make predictions about future sales. This can be valuable in developing and implementing sales strategies.

See salaries and explore the career path for Sales Analyst

Product Manager

Product Managers are responsible for the development and management of products. This course may be useful in providing Product Managers with a foundation in time series forecasting methods, which can be used to analyze historical sales data and make predictions about future demand. This can be valuable in making informed decisions about product development and marketing strategies.

See salaries and explore the career path for Product Manager

Business Analyst

A Business Analyst is responsible for bridging the gap between business and IT by analyzing business processes and developing solutions to improve efficiency and effectiveness. This course may be useful in providing Business Analysts with a foundation in time series forecasting methods, which can be used to analyze historical data and make predictions about future trends. This can be valuable in developing business strategies and making informed decisions.

See salaries and explore the career path for Business Analyst