Measure for XTD Variance

[Anonymous]

I am trying to create a measure to compare my actual daily values to my forecasted daily values to a dynamic date range.

For example, I have several sheets in my report that look at different date range views of the graph above. Some are YTD, some are QTD and some are snapshots of individual months. 

The green line are the daily actuals and the black line is the forecast. I need a measure to essential compute the variance up until the latest data point of actuals and ignoring future days in the forecast. 

[v-piga-msft]

Hi @Anonymous,

For your scenario, could you please share some data sample which could reproduce your scenario and your desired output so that we could have a test on it and get the solution more quickly.

Best  Regards,

Cherry

[Anonymous]

@v-piga-msft 

Hi Cherry,

Here is a small sample of data that is in my data set.

Year	Month	Day	Sum of Net Forecast Production	Sum of Net Actual Production
2019	April	1	0	0
2019	April	2	0	0
2019	April	3	0	0
2019	April	4	0	0
2019	April	5	0	0
2019	April	6	0	0
2019	April	7	0	0
2019	April	8	0	0
2019	April	9	0	257.93
2019	April	10	0	330.48
2019	April	11	0	1301.87
2019	April	12	0.03	702.18
2019	April	13	170.04	752.76
2019	April	14	258.07	1555.37
2019	April	15	339.19	1887.92
2019	April	16	420.31	2274.54
2019	April	17	611.27	2288.46
2019	April	18	726.33	2915.12
2019	April	19	814.97	3433.32
2019	April	20	904.01	3528.04
2019	April	21	993.47	3938.37
2019	April	22	1083.35	3540.84
2019	April	23	1173.7	3733.62
2019	April	24	1264.53	4251.15
2019	April	25	1355.87	5872.98
2019	April	26	1447.74	5677.79
2019	April	27	1540.17	6619.26
2019	April	28	1633.22	7455.36
2019	April	29	1909.77	9296.28
2019	April	30	2369.86	7735.63
2019	May	1	6754.02	9649.03
2019	May	2	6891.29	9605.51
2019	May	3	7660.63	9759.23
2019	May	4	8065.61	10867.72
2019	May	5	8195.83	10167.88
2019	May	6	8322.87	9944.31
2019	May	7	8433.18	10478.11
2019	May	8	8541.48	10476.03
2019	May	9	8579.13	10316.17
2019	May	10	8592.3	11000.39
2019	May	11	8602.57	9784.22
2019	May	12	8613.27	10675.46
2019	May	13	8628.2	9394.35
2019	May	14	8634.65	9059.49
2019	May	15	8631.33	9580.24
2019	May	16	8625.75	9351.24
2019	May	17	8561.46	9603.78
2019	May	18	8473.62	9241.26
2019	May	19	8387.92	9000.09
2019	May	20	8304.25	9211.22
2019	May	21	8222.54
2019	May	22	8142.73
2019	May	23	8064.76
2019	May	24	7988.53
2019	May	25	7913.98
2019	May	26	7841.08
2019	May	27	7769.75
2019	May	28	7699.96
2019	May	29	7631.61
2019	May	30	7564.68
2019	May	31	7499.13