# Data Forecasting

# Overview

ATSD includes a range of univariate forecasting algorithms that predict future series values based on historical data.

Supported algorithms:

• Holt-Winters
• ARIMA
• Singular Spectrum Analysis

The accuracy of predictions depends on the frequency of data collection, the selection interval, and the algorithm parameters.

Forecasting Example with Abnormal Deviation:

# Reference

• Editor Settings
• Editor Tools
• Using Forecasts

# Editor Settings

Enable data forecasting on the Data > Forecasts page.

# General Settings

Enabled forecasts are prepared by background jobs on schedule according to cron expressions. Forecasting jobs are typically executed during off-peak hours.

# Data Selection Settings

For data exclusion options, see Calendar Exception Settings.

# Aggregation Settings

# Algorithm Parameters

# Persistence Settings

# Editor Tools

Editor Tools are located at the bottom of the Forecast Configuration page.

Tool	Description	Example
Calculate Parameters	Click Calculate Parameters to calculate algorithm parameters.
Run	Execute the forecast job. Use this tool to test a forecast. The response contains the number of calculated forecasts.
Export	Export forecast data in CSV format.
Show Meta	Display parameters used to calculate the forecast. Metadata is stored with the forecast. Collection interval is an interval within the real data extracted to build the forecast.

Use the split-button on the Data > Forecasts page to add Exceptions and perform Testing.

# Using Forecasts

# Rule Engine

• Access pre-computed forecasts using forecast functions.
• Use forecast values as thresholds to trigger response actions if observed values deviate from forecast values by some amount.
• Compare forecast values to simple, weighted, and exponential moving averages.

abs(avg() - forecast().interpolated) > 25

This expression compares the moving average of some metric to the forecast and alerts if the absolute difference exceeds 25.

thresholdTime(null, 90, '1 DAY') != null

• thresholdTime function returns Unix time in milliseconds when the forecast is expected to exceed 90 for the first time.
• The condition becomes true if the expected violation is to occur in less than 1 day.

# Ad hoc Export

Set Data Type setting to Forecast, optionally specify a forecast name:

# Data API

Query and insert forecast values with Data API. The insert capability can be used to populate the database with custom forecast values calculated externally.

A sample forecast JSON query:

[
    {
        "entity": "nurswgvml007",
        "metric": "cpu_busy",
        "type": "FORECAST",
        "endDate": "now + 2 * hour",
        "startDate": "now "
    }
]

[
  {
    "entity": "nurswgvml007",
    "metric": "cpu_busy",
    "tags": {},
    "type": "FORECAST",
    "aggregate": {
      "type": "DETAIL"
    },
    "meta": {
      "timestamp": "2018-05-15T08:20:00.000Z",
      "averagingInterval": 600000,
      "alpha": 0,
      "beta": 0.4,
      "gamma": 0.4,
      "period": {
        "count": 1,
        "unit": "DAY"
      },
      "stdDev": 7.224603272075089
    },
    "data": [
      {"d":"2018-05-15T08:20:00.000Z","v":11.604692968987015},
      {"d":"2018-05-15T08:30:00.000Z","v":14.052095586152106},
      {"d":"2018-05-15T08:40:00.000Z","v":15.715682104344845},
      {"d":"2018-05-15T08:50:00.000Z","v":11.604018743609409},
      {"d":"2018-05-15T09:00:00.000Z","v":12.507966355503251},
      {"d":"2018-05-15T09:10:00.000Z","v":12.59619153186056},
      {"d":"2018-05-15T09:20:00.000Z","v":11.092825413101579},
      {"d":"2018-05-15T09:30:00.000Z","v":11.747112803805937},
      {"d":"2018-05-15T09:40:00.000Z","v":11.137962830355074},
      {"d":"2018-05-15T09:50:00.000Z","v":11.40358025413789},
      {"d":"2018-05-15T10:00:00.000Z","v":16.728103701429056},
      {"d":"2018-05-15T10:10:00.000Z","v":12.75646043607565}
    ]
  }
]

Insert a forecast into ATSD using POST method:

POST /api/v1/series/insert

Payload:

[
    {
        "entity": "nurswgvml007",
        "metric": "mpstat.cpu_busy",
        "type": "FORECAST",
        "data": [
            {
                "t": 1462427358127,
                "v": 52
            }
        ]
    }
]

Additional examples:

• Insert Named Forecast
• Insert Forecast Deviation

# Charts

Load forecasts into charts by setting data-type = forecast in the [series] section.

[series]
    entity = nurswgvml007
    metric = cpu_busy
    data-type = forecast

List of widget and series settings applicable to forecast data:

Name	Description	Example
data-type	Data type for the current series. Possible values: history, forecast, forecast_deviation, lower_confidence, upper_confidence. Example: data-type = forecast
forecast-name	Unique forecast identifier. Useful when creating multiple forecasts for the same series. Usually used to view imported forecasts generated with external tools. If no forecast name is set, the default forecast is loaded. Example: forecast-name = hw5
forecast-style	CSS styles applied to forecasts in column and column-stack modes. Example: forecast-style = stroke: magenta;
style	Render forecast as a solid line instead of dashed line. Example: style = stroke-dasharray: none;