Skip to main content Accessibility help
Internet Explorer 11 is being discontinued by Microsoft in August 2021. If you have difficulties viewing the site on Internet Explorer 11 we recommend using a different browser such as Microsoft Edge, Google Chrome, Apple Safari or Mozilla Firefox.

Chapter 18: Forecasting from Time Series Data

Chapter 18: Forecasting from Time Series Data

pp. 487-516

Authors

, Central European University, Vienna and Budapest, , University of Michigan, Ann Arbor
Resources available Unlock the full potential of this textbook with additional resources. There are free resources and Instructor restricted resources available for this textbook. Explore resources
  • Add bookmark
  • Cite
  • Share

Summary

Your task is to predict the number of daily tickets sold for next year in a swimming pool in a large city. The swimming pool sells tickets through its sales terminal that records all transactions. You aggregate that data to daily frequency. How should you use the information on daily sales to produce your forecast? In particular, how should you model trend, and how should you model seasonality by months of the year and days of the week to produce the best prediction?

About the book

Access options

Review the options below to login to check your access.

Purchase options

eTextbook
US$72.00
Hardback
US$184.00
Paperback
US$72.00

Have an access code?

To redeem an access code, please log in with your personal login.

If you believe you should have access to this content, please contact your institutional librarian or consult our FAQ page for further information about accessing our content.

Also available to purchase from these educational ebook suppliers