Hello
I'm using SQL Server BI Development Studio to create a mining structure to forecast time series analysis.
The process to create a model for time series has these steps:
Create a connection to the Database;
Create a source view. Create relationships between tables using the foreign keys;
Create the model with the mining structure. Define the fields that I won’t to predict and the algorithm properties aren’t changed.
Is something wrong in these steps, I missing something?
Other problem is the utilization of the model created earlier. In some tests that I made, the models don’t do forecasting upper then 20 steps. The problem could be the number of lines? But other data set with a large number of null values doesn’t have any problem predicting more than 20 steps using the time series algorithm.
Can someone help me?
Thanks in advance.
Jo?o Santos
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I don't think there is a problem. The nature of the time series algorithm we use - Auto Regression Trees with Cross Predict (ARTXP) - is that predictions in the future can become unstable, since predictions become dependent on predictions. When we detect this instability, we stop producing output - this behavior can not be changed.
The dataset with large numbers of nulls is using some sort of missing value substitution which actually smooths the series - although you will have to determine the accuracy (usually more data = better).
We are considering allowing users to control the "instability" threshold in future versions.
|||The time series algorithm has a mechanism for detecting instability in predictions and stops returning predicted values when the variance exceeds a certain threshold. The point at which this happens is data-dependent - this explains the behavior you're seeing.
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