Show the implementation of Time Series Algorithm.
The Microsoft
Time Series algorithm provides regression algorithms that are optimized for the
forecasting of continuous values, such as product sales, over time. Whereas
other Microsoft algorithms, such as decision trees, require additional columns
of new information as input to predict a trend, a time series model does not. A
time series model can predict trends based only on the original dataset that is
used to create the model. You can also add new data to the model when you make
a prediction and automatically incorporate the new data in the trend analysis.
The following
diagram shows a typical model for forecasting sales of a product in four
different sales regions over time. The model that is shown in the diagram shows
sales for each region plotted as red, yellow, purple, and blue lines. The line
for each region has two parts:
Historical
information appears to the left of the vertical line and represents the data
that the algorithm uses to create the model.
Predicted
information appears to the right of the vertical line and represents the
forecast that the model makes.
The combination
of the source data and the prediction data is called a series.
A time series
model has a single parent node that represents the model and its metadata.
Underneath that parent node, there are one or two time series trees, depending
on the algorithm that you used to create the model.
The Microsoft
Time Series algorithm includes two separate algorithms for analyzing time
series:
The ARTXP
algorithm, which was introduced in SQL Server 2005, is optimized for predicting
the next likely value in a series.
The ARIMA
algorithm was added in SQL Server 2008 to improve accuracy for long-term
prediction.
If you create a
mixed model, two separate trees are added to the model, one for ARIMA and one
for ARTXP. If you choose to use only the ARTXP algorithm or only the ARIMA
algorithm, you will have a single tree that corresponds to that algorithm. You
specify which algorithm to use by setting the FORECAST_METHOD parameter.
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