Transcript Forecasting with the Microsoft Time Series Data Mining Algorithm

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Time Series Forecasting
The development of data mining
models to predict future values based
on previously observed values
Design ► Process ► Query
Mining Model
Design ► Process ► Query
Mining Model
Data
Mining
Engine
Training Data
Design ► Process ► Query
Mining Model
Data
Mining
Engine
Predicted Data
Data to Predict
Simple Forecasting by Using the
Table Analysis Tools Excel Add-In
ARTxp Algorithm
Exploring Different ARTxp Scenarios by
Using the Data Mining Client Excel Add-In
ARIMA Algorithm
Columnar Format
Key time column
Predictable columns
Interleaved Format
Key time column
Series
Data value
PredictTimeSeries(<table column reference>, n)
Examples
SELECT FLATTENED PredictTimeSeries([MSFT], 3) AS Forecast
FROM [StockForecast]
SELECT FLATTENED PredictTimeSeries([Price], 3) AS Forecast
FROM [StockForecastInterleaved]
WHERE [Series] = 'MSFT'
Querying a Mining Model by Using Excel
Additional Scenarios
SELECT FLATTENED PredictTimeSeries([MSFT], -6, 0) AS Forecast
FROM [StockForecast]
Additional Scenarios (Continued)
Additional Scenarios: Replacing Cases
Additional Scenarios: Extending Cases
Querying a Mining Model by Using
SQL Server Management Studio
SELECT FLATTENED
(SELECT *, PredictStDev() AS Stdev, PredictVariance() AS Var
FROM PredictTimeSeries([MSFT], 3)) AS Forecast
FROM [StockForecast]
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PREDICTION_SMOOTHING Scenarios
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End-to-end Forecasting by Using SSDT and DMX
(Continued)
Forecasting by Using a SQL CLR Function
http://www.microsoft.com/en-us/sqlserver/solutions-technologies/business-intelligence/predictiveanalytics.aspx
http://msdn.microsoft.com/library/bb510517.aspx
http://www.sqlserverdatamining.com
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