Time series analysis
You'll learn how to identify trends and seasonal patterns in historical data to forecast future sales and costs. Time series analysis uses moving averages to smooth out fluctuations and reveal the underlying trend, then adjusts for seasonal variations using either additive or multiplicative models.
This lesson is part of a paid course
Buy the course to unlock every video lesson and the AI Tutor, plus mock exams with feedback on CIMA courses.
Already bought? Sign in
Ask the AI tutor anything about this lesson
It knows every lesson in the course — explanations, clarifications, practice questions.
In this lesson
- 0:00 Time series analysis introduction
- 3:31 Identifying trends using moving averages
- 6:03 Calculating three-point moving averages
- 9:09 Seasonal variations and time series components
- 10:04 Additive and multiplicative models explained
- 12:03 Forecasting with trend and seasonal adjustment
- 14:51 Limitations of time series analysis
About this lesson
This technique is essential for Performance Management and appears across multiple CIMA and ACCA papers where forecasting is required. By mastering time series analysis, you'll be able to make reliable predictions based on past patterns and understand when this approach has limitations.
Duration: 16m
Transcript
Every lesson comes with a full searchable transcript - included with the course.
Get the Management Accounting course
All 87 video lessons, an AI Tutor in every lesson and lifetime access.
Already bought? Sign in