You can compare across time periods without using a formula.
Estimated reading time: 4 minutes

A time series is a set of data points ordered by time. For example, within the same week a sale on Tuesday comes before a sale on Wednesday in such a data set. You can use ThoughtSpot’s time series analysis feature to search for answers about series data.

You might use this feature to compare a time period across other time periods. For examples sales for each month across several years. You may also want to calculate an aspect such as growth over the same time period across other periods. You can also do relative analysis such as sales for the last 3 months of each year across years.

You can use one or more of the following period keywords to create this type of analysis:

Period keywords

Keyword Example
quarter (date) quarter (purchase date)
quarter of year (date) quarter of year (purchase date)
month of quarter (date) month of quarter (purchase date)
week of year (date) week of year (ship date)
week of quarter (date) week of quarter (ship date)
week of month (date) week of month (ship date)
day of year (date) day of year (ship date)
day of quarter (date) day of quarter (ship date)
day (date) day (ship date)
day of month (date) day of month (order date)
day of week (date) day of week (order date)
hour (datetime) hour (timestamp)

All of these keywords sort the data using datetime semantics, that is chronologically in a time sequence. By default, the Search bar suggests these keywords less frequently than others.

You can use these new keywords in combination with the existing data keywords which are:

  • Detailed
  • Hourly
  • Daily
  • Weekly
  • Monthly
  • Quarterly
  • Yearly

Examples of time series analysis

Typically, when you search for answers about series data, the visualizations that answer your questions are line charts. These charts frequently but not always include a stack to indicate a period.

When you search for an aspect of data of time series, a typical response is a line chart showing the aspect as it rises and falls over time.

You can also add a relative date filter for example,

total revenue quarterly yearly by year month(commit date) >= 01/01/1995
month(commit date) before 01/01/1998

This type of query also yields a stacked line chart:

The child date time attribute is on the X-axis and the parent in the legend. For example, if you search revenue month yearly the child, monthly, appears on the x-axis and the parent, yearly, in the legend.

Granularity for date filters

You can refine a simple date filters by adding hierarchical date filter to your query. The ability to specify two bucket granularities such as “hour of day” or “week of year” are two examples. The syntax of this type of query is

small_bucket of big_bucket [INTEGER_CONDITION]

The INTEGER_CONDITION is optional but it must be an integer. For example, this query is valid:

revenue by day of week <= 2

This query is invalid:

revenue by day of week = Tuesday

You can specify one or more granular filters.

These tips and gotchas apply to time granularity:

  • The system-defined fiscal rules are respected. This means, for example, if the fiscal year begins in February, month of year = 2 matches dates in March.
  • Fiscal shorthands such as Q1, Q2 and so on are not supported, so day of week = d1 is not valid.
  • INTEGER_CONDITION with = or != accept a list of filter values, so, day of week = 1 2 3 is valid.
  • INTEGER_CONDITION with = or != require legal values, so day of week > accepts any integer on the right hand side while day of week = requires a value in the legal 1-7 range.
  • Simple date filters allow you to use edit the filter through the answer to refine your search, adding a a hierarchical date filter in the search bar disables this ability.