I am trying to create a forecast but this is the error that I get:
I am working with about 300,000 rows of data. Most of the report has already been built. My data just doesn't cotain certain dates. How can I solve this issue?
So the issue boils down to the problem of "How to create an evenly spaced timeline". You can easily achieve this in PowerQuery
Create a separate daily date table.
Outer join your observations onto the dates, which will give you "null" for the unobserved days
Apply the "fill down" operation on your values column, which basically means that the last value will be repeated until a new observation appears.
These evenly distributed time series is suitable for ML forecasting, at least when it comes to predicting trends. But the real power of this feature in Power BI is in predicting seasonality, and you most likely won't get that right with the above interpolation.
Related
I am trying to recreate a Tableau table view in Power BI where I can compare two customizable date ranges and show the percent differences across metrics as a calculated row.
Reference Screenshot from Tableau
In practice, my table will have 3 rows: 1 row for each time period selected and a row for the percent difference. The percent difference row is a nice to have, not a need. All my table metrics are coming from the same source.
I also need to set up two different date slicers that each row of my table will reference. I've played around a little with setting up a separate date table for the comparison period to be selected from, following this thread: Comparing Data Across Date Ranges
The challenge now is showing two separate periods in the same table.
Appreciate any guidance!
I have the following data currently in PowerBI
This is totals for multiple entities that are under the same location (ID 1).
What I would like to produce is the following
Which is the summation of the 3 totals over time, with start and end dates of when they applied.
I will eventually try to use this to show a trending chart over time for how the totals changed.
Is something like this even possible in PowerBI and/or DAX to first produce the results and then report a trend line like that? The trend line in this example would just have the 3 data points.
The only thing I can think of right now is to extrapolate out each range to 1 day at a time per original screenshots rows, and make the granularity of the chart daily, instead of ranges like this. Then the summation becomes a lot simpler as its just be ID and Date. Only concern is the data volumes that will be produced by extrapolating this out by days like that.
To preface this, I'm fairly experienced in Excel and VBA but new to PowerBI and more than a bit confused.
I have a flat table with a [creationdate]-, [Prio] (Priority (1,2,3)) and a calculated [Days Open] column, among many irrelevant others. I need to create a chart that displays the average days a case was open by priority of the case.
To display the average "days required" per (opening-) month for the past 18 months, I created the following measure:
Prio 1 = CALCULATE(AVERAGE('SourceName'[Days Open]),'SourceName'[Prio]=1)
Then I used that as a value, and used the [creationdate] as the x-axis. (Later I changed the x-axis to a new date table linked to [creationdate] without it making a difference.) To display this as monthly averages, I used the hierarchy limited to years and months, and went down one level in the chart.
Something seemed off so I checked first in Excel, then in the data source in PowerBI and yep: The averages in the PowerBI chart are complete bullshit.
Where did I go wrong? I assume it has something to do with the date hierarchy... So I created a date table as recommended (which....why?!) and linked it. That didn't make a difference.
Meanwhile in the data panel if I filter by the date column and calculate the average with the filtered selection of numbers externally, everything works as expected, so its not like there's a date formatting issue.
Do I have to create a calculated column with something akin to
DATE(YEAR([DateColumn]),MONTH([DateColumn]),1)
, then use that as the x-axis without the hierarchy, and hope nobody cares about the day in the label? Or is there something wrong with the measure used? I'm completely lost.
In PowerBI I'd like to build Non-standard matrix very similar to the report in Google Analytics.
What do I have now:
I want to change my subtotal to measure, which is calculated as the difference in percentage of the two values
What I want to get:
In Power BI, there is no way to override the subtotals of a matrix with a calculation. Part of the challenge is that you know there are only two date ranges, but as far as Power BI is concerned, there could be any number of date ranges.
It's difficult to tell from your question exactly what input you have and what output you're looking for. Further, the numbers in your screenshots are obscured. However, one consideration would be to solve the problem using measures (i.e. a measure representing the first date range, a measure representing the 2nd date range, and then a measure calculating the difference between them). You may need to change the layout of your visual a little to make this work and the specific design would depend on how static your date ranges are.
I am working on a report that has data by month. I have created a measure that will calculate a cost per unit which divides the sum of dollars by the sum of production volume for the selected month(s):
Wtd Avg = SUM('GLData - Excel'[Amount])/SUM('GLData - Excel'[Production])
This works well and gives me the weighted average that I need per report category regardless of if I have one or multiple months selected. This actual and budget data is displayed below:
If you take time to total the actual costs you get $3.180. Where I am running into trouble is a measure to sum up to that total for a visual (This visual does not total sadly). Basically I need to sum the aggregated values that we see above. If I use the Wtd Avg measure I get the average for the total data set, or .53. I have attempted another measure, but am not coming up with the correct answer:
Total Per Unit Cost = sumX('GLData - Excel','GLData - Excel'[Wtd Avg])/DISTINCTCOUNT('GLData - Excel'[Date])
We see here I return $3.186. It is close, but it is not aggregating the right way to get exactly the $3.180:
My Total Per Unit Cost formula is off. Really I am simply interested in a measure to sum the post aggregated Wtd Avg measure we see in the first graph and total to $3.180 in this example.
Here is my data table:
As you probably know already, this is happening because measures are dynamic - if you are not grouping by a dimension, they will compute based on the overall table. What you want to do is to force a grouping on your categories, and then compute the sum of the measure for each category.
There are 2 ways to do this. One way is to create a new table in Power BI (Modeling tab -> New Table), and then use a SUMMARIZE() calculation similar to this one to define that table:
SUMMARIZE('GLData - Excel',[Category],[Month],[Actual/Budget],"Wtd Avg",[Wtd Avg])
Unfortunately I do not know your exact column names, so you will need to adjust this calculation to your context. Once your new table is created, you can use the values from that table to create your aggregate visual - in order to get the slicers to work, you may need to join this new table to your original table through the "Manage Relationships" option.
The second way to do this is via the same calculation, but without having to create a new table. This may be less of a hassle. Create a measure like this:
SUMX(SUMMARIZE('GLData - Excel',[Category],[Month],[Actual/Budget],"Wtd Avg",[Wtd Avg]),[Wtd Avg])
If this does not solve your issue, go ahead and show me a screenshot of your table and I may be able to help further.