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:

Line chart showing revenue trend with a dashed line extending into future quarters and a shaded area representing the confidence interval of the 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.

      Line chart with one metric trended by time, with order amount plotted on the y-axis and date on the x-axis. Forecast toggle is not yet active.

    • The data contains no missing values.

  3. Under Configuration, toggle on Forecasting.

    Configuration panel with the Forecast toggle switched on. Forecast settings include period, confidence level, and optional seasonality.

    • 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.

    Forecasted line chart with future values shown as a dashed line and surrounding shaded region indicating a 95 percent confidence interval.

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:

Scatter plot using clustering to group data points. Each cluster is color-coded and labeled in the legend.

Steps:

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

    Basic scatter plot with uniformly colored data points. The x-axis represents the sum of order unit cost, and the y-axis represents the sum of order unit price. All data points are plotted in the same color without cluster grouping.

  2. Under Configuration, toggle on Cluster.

    Configuration panel with the Cluster toggle enabled. Below the toggle are inputs for cluster amount and threshold, which control grouping sensitivity.

    The clusters are highlighted:

    Scatter plot displaying five color-coded clusters labeled Cluster 0 to Cluster 4. Points are grouped based on similarity in order unit cost and price.

    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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