![]() Thus all prediction can be completed in SSAS and used in SSRS. But I just wonder if SSAS can be used in SSRS. I just wonder if prediction in SSAS can be used in SSRS. Why bring in extra code/complexity to solve the problem? If you really want to remove the calculation in SSRS (so it's only a front-end), I'd look at building a Python or R model and use SQL Server Machine Learning Services. First of all, the data mining in SSAS is deprecated, so I would avoid using it in new projects: .Īs you have read in this tip, setting up simple linear regression with T-SQL is straight forward. However, if you have to start new, I don't think it's easier. You probably can query that model using DMX. If you already have a linear regression model set-up in SSAS for the exact same data, you can probably use it. Tuesday, Septem2:11:51 AM - Koen Verbeeck The second part may answer the first- The field in your dataset corresponds with the month number is that a necessary requirement? I'd have thought not but this is rather new to me still so learning. ![]() If I SUM() these, I suspect I will be getting wildly different slopes than what should be present. For instance, I have a dataset that uses a date value combined with a sequence number for the 'ID' column, so values like 4411416, 4422427, 1702103, 1199776, 1297663 are common. up to the end of the recordset (essentially row numbers)? I ask as an actual 'ID' column will seldom be so concise. In this very informitive 'tip' one of the 'sums' is = SUM(ID) - my question is this: Does this speicific value require values 1,2,3,4. I have a question (with two parts) that I'm hoping someone more famliar with the underlying math can help me figure out. It's not the X-values themselves that are very important, it's their relation with the corresponding Y-values. But the values of the X-axis don't need to be like an identity column. In this tip I used the month numbers because it's easy to visualize. This prevents SSRS from having a gap between the trendline and the forecast line. Month of the original data set (September in our example), we calculate the forecast as well. If the trend is not known, we calculate the forecast using the slope and the intercept. If the trend isĪlready known for a month, we keep the forecast empty. LEFT JOIN #Temp_Regression r ON c.ID = r.ID įirst we create a list of all the months we want on the graph, including months not provided by the original data set. WHEN Trend IS NULL AND c.ID > (SELECT MAX(ID) FROM #Temp_Regression) Only for the last 3 months of the year in this example. Suppose we don't have values for January, then we don't want to calculate However, we also need to check if the month for which we calculate the forecast comes after If trend is not found, it means we can calculate a forecast. WHEN Trend IS NOT NULL AND c.ID = (SELECT MAX(ID) FROM #Temp_Regression) This prevents a gap in the line charts in SSRS. value as the trendline (instead of NULL). For the last actual value (September in this example), we want forecast to have the same ,Forecast = CASE WHEN Trend IS NOT NULL AND c.ID (SELECT MAX(ID) FROM #Temp_Regression) elaborate function to get the short month name and yearĮLSE CONCAT(LEFT(DATENAME(MONTH,CONVERT(DATE,CONCAT('2014','-',c.ID,'-','01'))),3),' 2014') SELECT TOP 12 ID = ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) In this example, ID is the x-coordinate and OrderQuantity the y-coordinate. SELECT ID = 9, OrderMonth = 'Sep 2014', OrderQuantity = 63, Trend = CONVERT(DECIMAL(38, 10),NULL) SELECT ID = 8, OrderMonth = 'Aug 2014', OrderQuantity = 70, Trend = CONVERT(DECIMAL(38, 10),NULL) SELECT ID = 7, OrderMonth = 'Jul 2014', OrderQuantity = 65, Trend = CONVERT(DECIMAL(38, 10),NULL) ![]() SELECT ID = 6, OrderMonth = 'Jun 2014', OrderQuantity = 58, Trend = CONVERT(DECIMAL(38, 10),NULL) SELECT ID = 5, OrderMonth = 'May 2014', OrderQuantity = 53, Trend = CONVERT(DECIMAL(38, 10),NULL) SELECT ID = 4, OrderMonth = 'Apr 2014', OrderQuantity = 50, Trend = CONVERT(DECIMAL(38, 10),NULL) SELECT ID = 3, OrderMonth = 'Mar 2014', OrderQuantity = 60, Trend = CONVERT(DECIMAL(38, 10),NULL) SELECT ID = 2, OrderMonth = 'Feb 2014', OrderQuantity = 41, Trend = CONVERT(DECIMAL(38, 10),NULL) SELECT ID = 1, OrderMonth = 'Jan 2014', OrderQuantity = 45, Trend = CONVERT(DECIMAL(38, 10),NULL) Trend is an empty column that will hold the calculated linear regression later on. IF OBJECT_ID('Tempdb.#Temp_Regression') IS NOT NULL
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