Data Forecasting

Overview

ATSD includes built-in forecasting algorithms that predict future series values based on historical data. The accuracy of these predictions depends on the frequency of data collection, the selection interval, and the algorithm used.

Supported algorithms for auto-regressive time series extrapolation are Holt-Winters and ARIMA.

Forecasting Example with Abnormal Deviation:

Reference

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.

Setting Description
Retention Interval Specifies how long a forecast is stored in the database. Forecasts that are older than current time, or End Time, if specified, minus Retention Interval are deleted.

Data Selection Settings

Setting Description
Metric Metric name for which forecasts are calculated.
Entity If selected, forecasts are calculated for the specified entity.
Supersedes Entity Group drop-down list.
If neither entity nor entity group is specified, forecasts are prepared for all entities.
Entity Group If selected, forecasts are calculated for entities contained in the specified entity group.
Tags Prepare forecasts only for series containing the specified series tags.
End Time End time of the Data Selection Interval and Series Selection Interval.
This field supports calendar expressions, for example current_day.
If not defined, the field is set to the time the job is run.
Data Selection Interval Time frame for selecting detailed data used as forecast input.
Specify the end of the interval in the End Time field, otherwise the end of the selection interval is set to current time.
Series Selection Interval Ignore any series with Last Insert Time before End Time by more than the specified interval.
Use this option to ignore series which have not been updated for a long time.
Calendar Ignore detailed values within the time intervals listed in the calendar.
Empty Period Threshold Ignore a series with a percentage of empty periods greater than the specified threshold.
Calculated as 100 * (number of empty periods before interpolation)/(total number of aggregation periods in Data Selection Interval).

For data exclusion options, see Calendar Exception Settings.

Aggregation Settings

Setting Description
Group By Grouping key for merging multiple series into one. Detailed data for multiple series sharing the same grouping key are merged into one array prior to computing aggregate statistics.
Auto Aggregate The server automatically calculates an aggregation period that produces the most accurate forecast, defined as having the lowest variance from observed historical data.
Aggregation Period Period of time over which the detailed samples are aggregated.
Aggregate Statistic Aggregation function applied to raw data to regularize the series. Aggregate values for empty periods without detailed data are interpolated as values of aggregate functions for previous periods.

Algorithm Parameters

Setting Description
Algorithm Holt-Winters or ARIMA forecasting algorithms.
Score Interval Part of Data Selection Interval that is used to compute variance between observed values and forecast to rank forecasts by variance.
The shorter the Score Interval, the more weight is assigned to recently observed values.
Auto Period The server automatically calculates seasonality of the underlying series that produces the most accurate forecast, defined as having the lowest variance from observed historical data.
Period Specify seasonality of the underlying series.
Auto Parameters The server automatically calculates algorithm parameters that produce the most accurate forecast, defined as having the lowest variance from observed historical data.

Persistence Settings

Setting Description
Forecast Name Optional name used to differentiate between several forecasts for one underlying series.
Use cases:
forecastName field in Data API
forecast(name) Rule Engine function
forecast-name Chart setting
Default Forecast Enable these settings instead of default settings when calculating on-demand forecast.
On-demand forecast is calculated at request time if a pre-stored forecast is not available.
Forecast Range Minimum and Maximum constraints applied to the stored forecast values to ensure that such values are within the specified range.
Constraints are applied to the winning forecast after scoring stage.
Forecast Interval The length of time into the future for which forecasts are prepared and stored in the database.
Can be rounded upwards to the nearest forecast period.

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 statistical function values such as moving averages or weighted averages.
abs(avg() - forecast()) > 25

This expression compares the actual average value of some metric to the forecast metric value and alerts if the absolute value of the 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 "
    }
]
Open collapsed menu to view response.
[
  {
    "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:

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;