Splunk® Machine Learning Toolkit

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Smart Forecasting Assistant

The Smart Forecasting Assistant enables machine learning outcomes for users with little to no SPL knowledge. Introduced in version 4.3.0 of the Machine Learning Toolkit, this new Assistant is built on the backbone of the Experiment Management Framework (EMF), offering enhanced time-series forecasting abilities. The Smart Forecasting Assistant offers a segmented, guided workflow with an updated user interface. Move through the stages of Define, Learn, Review, and Operationalize to load data, build your model, and put that model into production. Each stage offers a data preview and visualization panel.

This Assistant leverages the StateSpaceForecast algorithm which persists a model using the fit command that can be used with the apply command. StateSpaceForecast is based on Kalman filters, supports incremental fit, and automatically imputes any missing values in your data. To help improve the accuracy of your forecast, this algorithm includes the ability to account for the effects of specific days that need to be treated differently.

To learn more about the Smart Forecasting Assistant algorithm, see StateSpaceForecast algorithm.

Version 4.4.0 of the Smart Forecasting Assistant supports both univariate and multivariate forecasting . Version 4.3.0 of the Smart Forecasting Assistant is limited to univariate forecasting.

Smart Forecasting Assistant Showcase

You can gain familiarity of this new Assistant through the MLTK Showcase, accessed under its own tab. The Smart Forecasting Assistant Showcase examples include:

  • Forecast the Number of Calls to a Call Center
  • Forecast App Logons with Special Days
  • Forecast App Expenses
  • Forecast App Expenses from Multiple Variables

This image shows the landing page for the Machine Learning Toolkit Showcase page. The Forecast Time Series option is highlighted and pointing to the four end-to-end examples for the Smart Forecasting Assistant.

Click the name of any Smart Forecasting Showcase to see this new Assistant and its updated interface using pre-loaded test data and pre-selected forecast parameters.

Smart Forecasting Assistant Showcases require you to click through to continue the demonstration. Showcases do not include the final stage of the Assistant workflow to Operationalize the model.

Smart Forecasting Assistant univariate workflow

Move through the stages of Define, Learn, Review, and Operationalize to draw in data, build your model, and put that model into production.

This example workflow uses the call_center.csv dataset that ships with the MLTK. You can use this dataset or another of your choice to explore the Smart Forecasting Assistant and its features before building a model with your own data.

To begin, select Smart Forecasting from the Experiments landing page and the Create New Experiment button in the top right.

This image shows the Machine Learning Toolkit and the view under the Experiments tab. The Experiment types are displayed from which a user can create a new Experiment of that type. The new Experiment type of Smart Forecasting Assistant is highlighted with an arrow pointing to the Create new Experiment button.

Enter an Experiment Title, and optionally add a Description. Click Create to move into the Assistant interface.

This image shows the resulting modal window that generates following clicking the Create New Experiment button. Fields are filled in for Experiment Title and Description and a button labeled Create is highlighted in the bottom right corner of the modal window.

Define

Use the Define stage to select and preview the data you want to use for the forecast. You can pull in data from anywhere in the Splunk platform. You can use the Search bar to modify your data in advance of using that data within the Learn step.

This image shows the Define stage of the Assistant. The Search bar is highlighted and contains an inputlookup for the call center dataset.

As an alternative to accessing data via Search, you can choose the Datasets option. Under Datasets, you can find any data you have ingested into Splunk, as well as any datasets that ship with Splunk Enterprise and the Machine Learning Toolkit. You can filter by type to find your preferred data faster.

This image shows the Define stage of the Assistant. This view is if the alternate option to getting data into the Assistant called Datasets. The View Datasets menu is open with the call center dataset selected from the list

As with other Experiment Assistants, the Smart Forecasting Assistant includes a time-range picker to narrow down the data time-frame to a particular date or date range. The default setting of All time can be changed to suit your needs. Once data is selected, the Data Preview and Visualization tabs populate.

This image shows the Define stage of the Assistant. The menu option to change the default time range for the data from All time to another preset time frame or a custom time frame is open.

When you are finished selecting your data, click Next in the top right, or Learn from the left hand menu to move on to the next stage of the Assistant.

This image shows the Define stage of the Assistant. The left hand side menu option of Learn is highlighted. The green button labeled Next in the top right corner of the page is also highlighted.

Learn

Use the Learn stage to perform any preprocessing on your data, and to create your forecasting model. The Learn is made up of two menus: Initial data- search, and Smart Forecasting. The Initial data-search menu is a carry over from inputs made in the Define stage. The Smart forecasting menu is where you can make selections to customize and complete the forecasting outcome.

This image shows the Learn stage of the Assistant. In the body of the page, the Initial data menu is open showing the search string used to bring in data during the Define stage. A menu of available fields by which to build the forecasting model are highlighted on the left side of the page. Available fields include field to forecast, holdback period, and future timespan.

You can refer to the following table for details of each available field. Certain fields are required. Hovering over the question mark helper icons beside each field also provides field descriptions.

Field name Description
Field to forecast Required field. Select one (univariate) or more (multivariate) numeric field to forecast. You can select a maximum of five fields to forecast.
Holdback period Required field. Holdback is the number of data points held back from training to compare the forecast against known values. This comparison is done using R2 and RMSE statistics.
Future timespan Required field. Pick number of days you want to forecast into the future. The further into the future the forecast, the less accurate it is likely to be.
Confidence interval Required field. Specify and integer between 0 and 99, where a larger value means a greater tolerance for forecast uncertainty. Use the slider or open field to choose the value.
Special days field Optional field. Accessed using the Join special time entries preprocessing step. Special days data can improve your forecast by accounting for days which should be treated differently such as Black Friday sales or IP traffic on July 4th.
Period Optional field. Specify if the data has a known periodicity. The units of the period are equal to the span of the _time field. For example, hourly data may have a period of 24 (one day), whereas daily data may have a period of 7 (one week).
Notes Optional field. Use this free form block of text to track the selections made in the parameter fields. Refer back to notes to review which parameter combinations yield the best results.

You can select a maximum of five fields from the Fields to forecast drop-down list.

Once you make field selections, click Forecast to view results. Clicking Forecast produces a written summary at the top of the page, moves the Experiment into a Draft state, and makes the View History option available. View History allows you to track any changes you make in the Learn stage.

This image shows the Learn stage with field selections set under the Smart Forecasting menu. A visualization of the data is displayed on the Evaluate tab. A button in the top right of the page called View History is highlighted.

Leveraging the StateSpaceForecast algorithm provides the option to take company or business calendar specific days into account when building your forecasting model. Include these special days in your forecast using the Join special time entries preprocessing step option. For details on how to work with data from CSV lookups, see Define a CSV lookup in Splunk Web.

This image highlights the button within the Learn stage to add a preprocessing step. When hovered over, this button displays the text of Join special time entries.

The SPL button is available as a means to review the Splunk Search Processing Language being auto-generated for you in the background as you work through the Assistant.

This image shows the modal window generated from choosing the SPL button in the Assistant. As you work through the Assistant, SPL is written for you, and can be viewed, copied, and reused within the Splunk platform.

Choose to make further adjustments to field settings here, or click Next in the top right, or Review from the left hand menu to move on to the next stage.

This image shows the Learn stage of the Assistant. The button labeled Next in the top right corner is highlighted.

Review

Use the Review stage to explore the resulting model based on the fields selected at the Learn stage. The Review panels give you the opportunity to assess your forecasting results prior to putting the model into production.

Use the model statistics from R2 and RMSE to assess model accuracy and error rate. You can also choose to set a Forecasted Value date and time, an Earliest Threshold Violation alert date, as well as toggle the Confidence Interval on or off.

Navigate back to the Learn stage to make forecast adjustments, or click Save and Next to continue.

This image shows the Review stage of the Assistant. Four panels allow you to review your results prior to putting the model into production. Panels include R squared statistic, Root Mean Squared Error, Forecasted Value, and Earliest Threshold Violation. Both Forecasted Value, and Earliest Threshold Violation allow you to define dates and thresholds on screen. A Save and Next button in the top right is highlighted.

Clicking Save and Next generates a modal window that offers the opportunity to update the Experiment name or description. When ready, click Save.

This image shows the modal window generated from clicking the Save and Next button. This Save Experiment window allows you to give the Experiment an updated name or description.  A green button labeled Save in the bottom right of the window is pointed out with an arrow.

Operationalize

The Operationalize stage provides publishing, alerting, and scheduled training in one place. Click Done to move to the Experiments listings page.

This image shows the Operationalize stage of the Assistant. Options on this page include Publish Forecasting Models, Create Alert, Manage Alerts, Schedule Model Training, and View Scheduled Training Jobs. A green button labeled Done in the top right of the page is highlighted.

The Experiments listing page provides a place to publish, set up alerts, and schedule training for any of your saved Experiments across all Assistant types including Smart Forecasting.

This image shows the page you land on after you mark your Experiment as Done. This is the Experiment list view page. The Experiment created in this document is listed, and options to Manage and Publish the Experiment are highlighted.

Smart Forecasting Assistant multivariate workflow highlights

Introduced in version 4.4.0 of the MLTK, you can perform both univariate and multivariate forecasting with the Smart Forecasting Assistant. Version 4.3.0 of the Assistant is limited to univariate forecasting.

The multivariate workflow is the same as univariate in most ways. Particular screens offer some key differences associated to a multivariate workflow. Highlights of the multivariate workflow include:

Workflow stage Highlight
Learn Field to forecast menu is a multi-pick of up to five fields.
The Evaluate view offers combined view of fields to forecast.
Review See the number of fields to forecast as well as fields by name.
Use the View fields menu to filter results shown.
Choose to see fields in a combined or split view.
Set the Earliest Threshold Violation values in one place or set individual thresholds in split view.
Toggle the confidence interval on or off for the combined chart or per chart in split view.
Operationalize When setting Alerts you can choose which target field to alert on, based on the fields selected when building the Experiment.

The maximum number of fields to forecast you can select for the Smart Forecasting Assistant is five.

Learn

Use the Learn stage to perform any preprocessing on your data, and to create your forecasting model. In the multivariate workflow, the Field to forecast menu is multi-pick up to five fields and the list of fields is populated based on your data.

This image shows the Learn stage of the Smart Forecasting Assistant. The Field to forecast menu is open showing the ability to select multiple fields.

Review

Use the Review stage to assess the forecast based on your selections at the Learn stage. The Review panels give you the opportunity to assess your forecasting results prior to putting the model into production.

See the total number of chosen fields to forecast as well as those fields by name in their own drop-down. Choose to review the forecast charts in a combined view of one chart or a split view. In the combined view you can set the Earliest Threshold Violations for the all fields to forecast on one screen. Settings selected are immediately reflected in the chart results. Toggle the confidence interval on or off for the entire chart using the combined chart view, or by individual chart in the split chart view.

This image shows the Review stage of the Smart Forecasting Assistant. The top of the page shows the plain English summary of the forecast including the number of fields used to forecast and the a names of those fields. The option to view fields as combined chart is highlighted. Also highlighted is the option on the chart to toggle the confidence interval on or off.

In the split view you can set the Earliest Threshold Violations by individual fields to forecast. Settings selected are reflected in the chart results when you click Apply.

This image shows the Review stage of the Smart Forecasting Assistant. The split chart view option is selected which has broken out the two fields to forecast into their own charts. One is for CRM and one is for ERP. The CRM chart is selected. Each chart has its own section to set the Earliest Threshold Violations.

In addition to the combined and split chart view options you can also customize fields by which to review results from the View fields drop-down menu.

This image shows the Review stage of the Smart Forecasting Assistant. The View fields area is highlighted to show a drop-down option by which you can filter the chart view of forecast results.

Operationalize

The Operationalize stage provides publishing, alerting, and scheduled training in one place. In a multivariate workflow you can choose which field to alert on, based on the fields selected when building the Experiment.

This image shows the Save As Alert modal that is accessed from the Operationalize stage. The field selector menu is highlighted, showing the ability to select from the list of fields chosen when building the Experiment. In univariate forecasting the one field is pre-populated for this alert setting.

Learn more

To learn about implementing analytics and data science projects using Splunk's statistics, machine learning, built-in and custom visualization capabilities, see the Splunk for Analytics and Data Science course.

Last modified on 09 April, 2020
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This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 4.5.0, 5.0.0, 5.1.0


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