What is time series analysis?

It is a study of data in particular periods or intervals. It may involve trend analysis, forecasting, and comparisons of data across various seasons and rolling time periods.

In this blog, we will build on our date and time calculation skills and progress to the more advanced time series analysis.

Here is the Time Series Quick Table Calculation:

Seasonality and year-over-year analysis

Seasonality:

Study of repetitive patterns in data

  • Discovers predictable trends
  • Determines high and low seasons
  • Improves forecasting decisions

Year-over-Year Comparison:

Comparison of results per discrete date intervals (e.g., month) across various years

Year-to-date and custom fiscal years

Year-to-date (YTD):

A Quick Table Calculation that Calculates from the start of the year to the last known data point in that year, other examples: MTD, QTD

Custom Fiscal Year:

In case we need to work with custom calendars, for example, a specific fiscal year, Tableau has a built-in option allowing us to set it up without the need for any additional mapping tables.

* It’s Possible to work with various year types or calendars in one workbook.

Calculating growth

Year over Year Growth:

A Quick Table Calculation (Table Down) that comparing change in a measure to the same period last year (E.g., Q1 2019 to Q1 2018)

*Requires discrete date dimensions

Calculation Behind:

(SUM([Sales])- LOOKUP(SUM([Sales]), -1))/ ABS(LOOKUP(SUM([Sales])-1))

Compound Annual Growth Rate – CAGR

A Quick Table Calculation that measures the average annual growth rate of a measure over a specified period longer than one year. it provides one aggregated number that defines the growth for the entire measurement period.

Calculation Behind:

POWER(ZN(SUM([Sales]))/LOOKUP(ZN(SUM([Sales])),FIRST()),ZN(1/INDEX()-1))-1

Moving (rolling) calculations

A Quick table Calculation across a specified number of values before and/or after the point in time and are mainly used to smoothen the fluctuations of highly granular data and to disclose the long-term trends, “the big picture”.

Calculation behind:

WINDOW_AVG(SUM([Sales]), Start, END)

Finally, combine various related time-series visualizations into a dashboard and make it more interactive.

Good Luck!

Read more: https://www.thedataschool.com.au/ayda-akbarzadeh/tableau-lod-expressions/

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The Data School
Author: The Data School