NRQL predictions

Overview

NRQL predictions in New Relic uses your time series' historical data patterns to predict or forecast future trends, providing insights into how metrics might behave in the future. This proactive approach helps you visualize trends and anticipate potential issues, enabling timely interventions to maintain optimal system performance. Predictions for your signal are generated by fitting a machine learning model to your historical data and using that model to predict into the future. This feature currently supports predictions for both seasonal (data that has a recurring, repeatable pattern on a set cadence) and non-seasonal time series. Metrics that exhibit a strong trend or defined seasonal pattern are best suited for predictions. These predictions are best utilized when added to existing dashboards to give a quick visual indicator as to where your metrics are headed.

Examples of situations where you might want to add predictions to your chart or dashboard:

• Your application is producing more logs over the past few hours and the disk space is running out.
• Your application is steadily running out of memory, threatening to crash the entire container.
• You need to project future revenue based upon observed seasonal and trend patterns.

While NRQL predictions themselves do not identify potential issues, they equip you with the information you need to assess trends and plan for future challenges. To generate alerts when the predicted values indicate an impending problem, you can use the Predictive Alerts feature. Predictive Alerts work alongside NRQL predictions to generate predictions for your metrics and alert when those predicted values pass a static threshold.

Pricing

The NRQL predictions is an Advanced Compute Product feature offered as an add-on or as a part of the Compute pricing model. Advanced CCUs are consumed when NRQL queries are executed for this feature.

Usage

NRQL predictions can be generated in two ways: either directly on charts you have already created or by writing a NRQL query using the PREDICT clause.

Add predictions to an existing chart.

You can add NRQL predictions only to charts that depict time series data, such as line or area charts. To add a NRQL prediction to an existing chart, perform the steps as follows:

Access a line or area chart

1. Go to one.newrelic.com > All capabilities.
2. To add predictions to a line or area chart, select it from Dashboards, or open it from Metrics and events.

Add predictions

To add a prediction to charts in dashboards, from the menu, select Predict trend. The Predict trend option is not available for charts in curated dashboards, which are system-generated and non-editable dashboards designed for specific services. So, from the menu, select View Query and add the PREDICT clause to the query.

In Data Explorer, the prediction appears on the chart highlighted in a gray area with a dashed line connecting the predicted values, representing the NRQL predictions default behavior. You can refine the query by fine-tuning according to your requirements and re-run it to get the updated predictions.

팁

When you add predictions to an area chart, it changes to a line chart containing the predictions.

Add to dashboard

To add a chart containing predictions to a dashboard:

1. Click Add to dashboard.
2. Enter a widget name.
3. Either select an existing dashboard or create a new one.

Use the PREDICT clause.

The NRQL syntax includes the PREDICT clause to streamline the use of NRQL predictions. For usage details and examples, refer to the default behavior section.

Example of using PREDICT in a NRQL query:

FROM Transaction SELECT count(*) WHERE error IS TRUE TIMESERIES PREDICT

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The default predictions on the chart appears as follows:

Default behavior

The PREDICT clause in a query comes with the following default behaviors which are dependent upon your query window duration (specified using the SINCE and UNTIL clauses):

Examples of queries with the default PREDICT clause.

Example queries and what the expected output is when PREDICT is used:

Example 1:

FROM Transaction SELECT sum(currentWorkerCount) TIMESERIES PREDICT

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Returns a time series chart with a default query window of one hour and predictions for the next 12 minutes. A total three hours of historical data is used for training (1 hour from the query window and 2 hours from the preceding 2 query windows).

Example 2:

FROM Transaction SELECT sum(currentWorkerCount) TIMESERIES SINCE 3 hours ago UNTIL 1 hour ago PREDICT

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Returns a time series chart with a query window of two hours (based upon the SINCE and UNTIL clauses) and predictions for the next 24 minutes. A total six hours of historical data is used for training (two hours from the query window and four hours from the preceding two query windows).

Customizing the predictions with BY, USING and SLIDE BY

You can customize the predictions by manually setting the model's seasonality and adding the BY, USING and SLIDE BY keywords.

Add the BY keyword.

Use the BY keyword to set how far ahead in time you want the model to predict. For example, PREDICT BY 3 hours generates predictions up to three hours into the future from the latest point in the time series. Make sure the time span is an integer unit of time. The maximum amount you can predict is 360 time windows (i.e., your time series window size times 360).

Example query with BY keyword that will generate predictions for the next 30 minutes:

FROM Transaction SELECT count(*) WHERE error IS TRUE TIMESERIES PREDICT BY 30 minutes

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Add the USING keyword.

Use the USING keyword to specify the amount of historical data you want to use to train the model. The total amount of data used is the sum of the query window duration (specified with the SINCE and UNTIL clauses) and value specified with the USING clause. For example, PREDICT USING 1 day uses the query window plus the data from the preceding day to train the model. Make sure the time span is an integer unit of time.

You can use up to 3,660 data points to train a model. If you exceed this limit, you'll receive an error message. You can determine how much time this represents by using the time series window. For example, with a 1-minute time window, you can use up to 3,660 minutes of training data, which equals 61 hours, or about 2.5 days.

Example query with the USING keyword with SINCE:

FROM Transaction SELECT sum(currentWorkerCount) SINCE 1 week ago TIMESERIES MAX PREDICT USING 2 weeks

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Add the SLIDE BY keyword.

Use the SLIDE BY keyword to reduce noise in your data when your forecast shows a lot of variation over short periods. This clause aggregates the data into overlapping time windows with a specified slide interval, smoothing out spikes in the chart and making it easier to identify trends.

Example query with the SLIDE BY keyword:

FROM Transaction SELECT count(*) PREDICT TIMESERIES 5 minutes SLIDE BY 1 minute

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Algorithm

Currently, NRQL predictions supports the Holt-Winters algorithm, also known as exponential smoothing, for both seasonal and non-seasonal time series. This is a commonly used and standard algorithm for forecasting and time series predictions.

Non-seasonal

For non-seasonal time series, only the level (average value) and trend (average increase or decrease) are modeled. Non-seasonal predictions are linear, so a flat line with or without a trend is expected.

Seasonal

For seasonal time series, the seasonal pattern is included in addition to the level and the trend. Seasonality represents a repeatable pattern that occurs on some periodic basis. We support automatic season detection of hourly, daily, and weekly seasonal lengths by default for the Holt-Winters model. In order to detect each seasonal length, you must ensure that you are providing enough data through a combination of the query window duration (specified using the SINCE and UNTIL clauses) and the USING clause. The minimum amount of data required for each seasonal length is specified below. By default, when no USING clause is used, NRQL predictions will use three times the query window to generate predictions.

You can specify custom seasonal lengths using the seasonality parameter.

FROM Transaction SELECT count(*) WHERE error IS TRUE TIMESERIES PREDICT holtwinters(seasonality: 1 hour)

FROM Transaction SELECT count(*) WHERE error IS TRUE TIMESERIES PREDICT holtwinters(seasonality: NONE)

FROM Transaction SELECT count(*) WHERE error IS TRUE TIMESERIES PREDICT holtwinters(seasonality: AUTO)

Advanced usage

Set optional Holt-Winters model hyperparameters.

중요

Adjusting the model parameters is intended for advanced users who want fine-grain control over the model and for recreating predictions generated with Predictive Alerts.

Currently, Holt-Winters is the only supported prediction algorithm and there are several hyperparameters that can be adjusted to fine-tune your predictions. The hyperparameters can be modified by adding holtwinters(<hyperparameter>: <value>) after the PREDICT clause in your query. Any combination of the following parameters can be added to modify how the model is fit to the historical data:

Hyperparameter	Description	Allowed values
alpha	The level smoothing factor where a larger value gives more weight to recent values.	[0, 1.0]
beta	The trend smoothing factor where a larger value gives more weight to recent values.	[0, 1.0]
gamma	The season smoothing factor where a larger value gives more weight to recent values. This parameter is not compatible with non-seasonal models and the query will return an error if you try to specify holtwinters(seasonality: NONE, gamma: <value>).	[0, 1.0]
phi	The trend damping parameter where a smaller value indicates more damping of the trend and a prediction that will flatten out.	[0, 1.0] It is recommended to use values only in the range of ([0.98, 1.0])

Example query with hyperparameter modifications:

FROM Transaction SELECT count(*) WHERE error IS TRUE TIMESERIES PREDICT holtwinters(alpha: 0.2, beta: 0.5, gamma: 0.5, phi: 0.99)

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Example query with the PREDICT clause specifying the seasonality, custom hyperparameters, and adding the BY and USING keywords:

FROM Transaction SELECT count(*) WHERE error IS TRUE TIMESERIES PREDICT holtwinters(seasonality: AUTO, alpha: 0.2) BY 1 hour USING 2 hours

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This query sets the seasonality to automatic detection and emphasizes historical data by adjusting the level smoothing factor. It predicts one hour into the future and trains the model using the query window length along with the previous two hours of historical data. The chart displays the customized predictions as follows: