Use Smart Functions

Smart functions represent our effort to leverage advanced statistical methods to help you gain more insights from your data with just a click of a button.

Forecasting

The forecasting function uses an autoregressive AR-X(p) model to create forecasts of future trends based on your data. Forecasting is supported for line charts:

Screenshot of a line chart that includes a forecast.

Steps:

  1. Create or open a line chart visualization in the Analytical Designer.

  2. Ensure that:

    • You are using only one metric and trending it by date.

      Screenshot of a line chart with one metric and trended over time.

    • The data contains no missing values.

  3. Under Configuration, toggle on Forecasting.

    Screenshot of the configuration tab showing the Forecasting option.

    • The number of predicted Periods must be smaller than the number of displayed data points.

    • The Confidence level determines the size of the shaded error region. A 95% confidence level means that the shaded region should be large enough to contain the predicted future data point 95% of the time.

    • Turn on Seasonality if your data is highly periodic to increase the accuracy of the forecast. For example, if your ice cream sales reliably grow every summer and plummet every winter. Note that if you enable this option, the number of predicted periods should be significantly smaller than the number of displayed data points.

    Screenshot of the resulting line chart with a forecast prediction.

Clustering

This function uses the BIRCH algorithm to group your data points into N clusters based on their inherent similarities, where N is defined by the user. Each cluster is color-coded for easy distinction. This clustering function is available for scatter plots:

Screenshot of a scatter plot that has its point highlighted using the clustering function.

Steps:

  1. Create or open a scatter plot visualization in the Analytical Designer.

    Screenshot of an ordinary scatter plot.

  2. Under Configuration, toggle on Cluster.

    Screenshot of the configuration tab showing the Forecasting option.

    The clusters are highlighted:

    Screenshot of a scatter plot with clustering.

    You can adjust the number of clusters.

    Additionally, you can adjust the threshold parameter of the BIRCH algorithm, which ranges between 0 to 1 (exclusive). A threshold closer to 0 results in more numerous, smaller clusters, making the algorithm more sensitive to minor variations in the data.

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