Time Intelligence in DAX: YTD, YoY, and Rolling Patterns That Work

“How are we doing versus last year?” is the question behind most Power BI reports. Time intelligence in DAX answers it — but only if your data model is set up correctly. This guide covers the setup and the five patterns that cover 95% of date-based reporting.

The non-negotiable: a proper calendar table

Time intelligence functions will silently return wrong results — or blank — if you don’t have a dedicated date table. The requirements:

1. One row per day, no gaps, covering the full date range of your fact data (and ideally a buffer on both ends).
2. A Date column of type Date (not DateTime, not text).
3. Marked as a date table — in Power BI Desktop, select the table → Table Tools → Mark as Date Table → choose the Date column.
4. A relationship from Date[Date] to your fact table’s date column (one-to-many, single direction).

You can generate this table in Power Query or DAX. The DAX approach is fastest to prototype:

Date = 
VAR _Start = DATE(2020, 1, 1)
VAR _End = DATE(2026, 12, 31)
RETURN
ADDCOLUMNS(
    CALENDAR(_Start, _End),
    "Year", YEAR([Date]),
    "Quarter", "Q" & FORMAT([Date], "Q"),
    "YearQuarter", FORMAT([Date], "YYYY") & " Q" & FORMAT([Date], "Q"),
    "Month", FORMAT([Date], "MMMM"),
    "YearMonth", FORMAT([Date], "YYYY-MM"),
    "MonthKey", YEAR([Date]) * 100 + MONTH([Date]),
    "Weekday", FORMAT([Date], "dddd"),
    "IsWeekend", WEEKDAY([Date], 2) > 5
)

If your fiscal year doesn’t start January 1, add a FiscalYear and FiscalQuarter column. Most time intelligence functions accept a fiscal year-end date as the optional third argument.

Pattern 1: Year-to-date (YTD)

Revenue YTD =
TOTALYTD(
    [Total Revenue],
    Date[Date]
)

TOTALYTD accumulates from January 1 of the current year through the latest date in the current filter context. Pair it with a month-level visual and you’ll see the running total reset each January.

Quarter-to-date and month-to-date work identically:

Revenue QTD = TOTALQTD([Total Revenue], Date[Date])
Revenue MTD = TOTALMTD([Total Revenue], Date[Date])

Pattern 2: Year-over-year (YoY)

Two equivalent approaches — pick whichever reads clearer to you:

// Approach 1: SAMEPERIODLASTYEAR
Revenue LY =
CALCULATE(
    [Total Revenue],
    SAMEPERIODLASTYEAR(Date[Date])
)

// Approach 2: DATEADD
Revenue LY =
CALCULATE(
    [Total Revenue],
    DATEADD(Date[Date], -1, YEAR)
)

Then compute the growth rate:

YoY Growth % =
VAR _Current = [Total Revenue]
VAR _Prior = [Revenue LY]
RETURN
DIVIDE(_Current - _Prior, _Prior)

Using variables (VAR) makes the formula readable and is slightly faster — the engine evaluates each variable once.

Pattern 3: Rolling 12-month average

Revenue Rolling 12M =
CALCULATE(
    AVERAGEX(
        DATESINPERIOD(
            Date[Date],
            MAX(Date[Date]),
            -12,
            MONTH
        ),
        [Total Revenue]
    )
)

Breaking this down:

• MAX(Date[Date]) gets the last date visible in the current filter context.
• DATESINPERIOD returns a 12-month window ending at that date.
• AVERAGEX iterates over each day in that window and averages the revenue.

A common mistake is using DATESINPERIOD inside CALCULATE directly without the iterator. That returns the sum, not the average — because CALCULATE applies the date range as a filter to the existing measure.

Pattern 4: Same period last year, year-to-date

Combine the two — show last year’s YTD figure next to this year’s, so February 2026 compares to February 2025’s YTD (not the full year 2025):

Revenue YTD LY =
CALCULATE(
    [Revenue YTD],
    SAMEPERIODLASTYEAR(Date[Date])
)

This is the pattern executives actually want. “How does our YTD through March compare to the same period last year?” — answered by one formula.

Pattern 5: Moving annual total (MAT)

The sum of the trailing 12 months, used to smooth out seasonality:

Revenue MAT =
CALCULATE(
    [Total Revenue],
    DATESINPERIOD(
        Date[Date],
        MAX(Date[Date]),
        -1,
        YEAR
    )
)

Distinct from the rolling 12-month average — MAT sums; rolling 12M averages. Choose based on the question you’re answering.

The five most common time intelligence mistakes

1. Forgetting to mark the date table. Time intelligence functions return blank or wrong numbers silently. Always verify the table is marked.
2. Using Date[Date] from the fact table instead of the calendar table. Time intelligence requires the calendar table’s date column.
3. Mixing TOTALYTD with manual date filters. TOTALYTD respects the current filter context, so a slicer on Date[Month] = "February" returns only February YTD — which is just February. Use ALL(Date) as the third argument if you want YTD to ignore the month slicer.
4. Comparing partial periods to full periods. “Revenue LY” for November 2025 returns November 2024 — correct. But “Revenue LY YTD” through November 2025 should return Jan–Nov 2024, not all of 2024. Pattern 4 above handles this.
5. Assuming SAMEPERIODLASTYEAR works without a calendar table. It needs a contiguous date range. Gaps in the calendar produce blank rows.

Reference: time intelligence function cheat sheet

Function	Returns	Typical use
TOTALYTD	Cumulative YTD value	Annual running totals
SAMEPERIODLASTYEAR	Date shifted -1 year	YoY comparison
DATEADD	Date shifted by N intervals	Flexible period shifts (quarters, months)
DATESINPERIOD	Rolling window	Rolling averages, MAT
PARALLELPERIOD	Equivalent prior period	QoQ, MoM comparisons
DATESMTD / DATESQTD / DATESYTD	Date table for MTD/QTD/YTD	Custom time calcs

What’s next

With these five patterns you can answer nearly every “vs. last year” question. The remaining 5% — fiscal calendars, 4-4-5 retail calendars, week-based time intelligence — requires custom date tables and the DATEADD approach rather than built-in functions. We’ll cover those in the advanced guide.

Pair these time intelligence measures with a well-designed star schema (see Star Schema Data Modeling) and your reports will be both fast and easy to maintain.