Beginning in SQL Server 2008 Standard, you can specify that which algorithm to use: You can also control the mix of algorithms to favor either short- or long-term prediction in the times series. However, as the time slices that you are predicting move further into the future, ARIMA is weighted more heavily. Because ARTXP is best for short-term predictions, it is weighted more heavily at the beginning of a series of predictions. The algorithm then blends the results of the two models to yield the best prediction over a variable number of time slices. The algorithm trains two separate models on the same data: one model uses the ARTXP algorithm, and one model uses the ARIMA algorithm. For a detailed explanation about the implementation of the ARTXP and ARIMA algorithms, see Microsoft Time Series Algorithm Technical Reference.īy default, the Microsoft Time Series algorithm uses a mix of the algorithms when it analyzes patterns and making predictions. Beginning in SQL Server 2008, the Microsoft Time Series algorithm added a second algorithm, ARIMA, which was optimized for long-term prediction. The ARTXP algorithm was optimized for short-term predictions, and therefore, excelled at predicting the next likely value in a series. In SQL Server 2005 (9.x), the Microsoft Time Series algorithm used a single auto-regressive time series method, named ARTXP. To correct for stores that do not accurately or consistently update sales data, they will create a general prediction model, and use that to create predictions for all regions. Additionally, the company can perform cross predictions to see whether the sales trends of individual bike models are related.Įach quarter, the company plans to update the model with recent sales data and update their predictions to model recent trends. By using the Microsoft Time Series algorithm on historical data from the past three years, the company can produce a data mining model that forecasts future bike sales. The company is especially interested in whether the sale of one bike model can be used to predict the sale of another model. The management team at Adventure Works Cycles wants to predict monthly bicycle sales for the coming year. You could train a general model on an average of all four regions, and then apply the model to the individual series to create more stable predictions for each region. For example, the predictions for a particular region are unstable because the series lacks good quality data. Cross prediction is also useful for creating a general model that can be applied to multiple series. For example, the observed sales of one product can influence the forecasted sales of another product. If you train the algorithm with two separate, but related, series, you can use the resulting model to predict the outcome of one series based on the behavior of the other series. The combination of the source data and the prediction data is called a series.Īn important feature of the Microsoft Time Series algorithm is that it can perform cross prediction. Predicted information appears to the right of the vertical line and represents the forecast that the model makes. Historical information appears to the left of the vertical line and represents the data that the algorithm uses to create the model. The model that is shown in the diagram shows sales for each region plotted as red, yellow, purple, and blue lines. The following diagram shows a typical model for forecasting sales of a product in four different sales regions over time. You can also add new data to the model when you make a prediction and automatically incorporate the new data in the trend analysis. A time series model can predict trends based only on the original dataset that is used to create the model. 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. The Microsoft Time Series algorithm provides multiple algorithms that are optimized for forecasting continuous values, such as product sales, over time. To learn more, see Analysis Services backward compatibility. Documentation is not updated for deprecated and discontinued features. Data mining was deprecated in SQL Server 2017 Analysis Services and now discontinued in SQL Server 2022 Analysis Services.
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