Serial chart - display seasonality

JayJohnsonWashoeCounty

I have a multi-year COVID dataset (# new cases per day).  Currently this is displayed as a traditional time series, incrementing on the x-axis by day (see attachment TraditionalTimeSeries.JPG).  I've been asked to now show this dataset in a way that allows the user to see potential seasonality, by having the x-axis as months and days (BUT NO YEAR) and the y-axis as the # New Cases per Day, regardless of year.  In this way, the chart would show the number of new cases on each calendar day "superimposed" for both 2020 and 2021.  I've attached a fairly ugly mock up of the concept (Seasonality.png). 

Any ideas on how I might accomplish this in Dashboard?  

TraditionalTimeSeries.JPG

Seasonality.png

JayJohnsonWashoeCounty

While the FeatureSet code suggested by @DominicRobergeIADOT looks promising, I couldn't make it work for me.  Instead I created a SQL view that somewhat creatively uses a calculated field "fakeDate" to push all the data into a single arbitrary year (1,000 AD to avoid potential confusion with any "real" datetime) and pushed the COVID New Cases for each year into separate attribute fields.  I published this view in a service and using this approach I can show the different years as series on a single chart and "fakeDate" as the Category axis.   

SQL View table:

Dashboard Serial Chart:

SQL View definition is shown below:

SELECT ROW_NUMBER() OVER(ORDER BY reportdt ASC) AS OBJECTID, reportdt, NewCases AS NewCases2020, null AS NewCases2021, DATEADD(year, -1020, reportdt) AS fakeDate
FROM COVID_CASESTABLE WHERE
(reportdt BETWEEN '2019-12-31' AND '2021-01-01')
UNION ALL
SELECT ROW_NUMBER() OVER(ORDER BY reportdt ASC) AS OBJECTID, reportdt, null AS NewCases2020, NewCases AS NewCases2021, DATEADD(year, -1021, reportdt) AS fakeDate
FROM COVID_CASESTABLE WHERE
(reportdt BETWEEN '2020-12-31'AND '2022-01-01')

This might get a bit cumbersome if we end up tracking COVID for 10 years, but in the meantime this was a quick, relatively simple workaround, if admittedly involving a bit of smoke and mirrors...