mirror of
https://github.com/honeymoose/OpenSearch.git
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6c313a9871
diskspace and creates a subfolder for storing data outside of Lucene
indexes, but as part of the ES data paths.
Details:
- tmp storage is managed and does not allow allocation if disk space is
below a threshold (5GB at the moment)
- tmp storage is supposed to be managed by the native component but in
case this fails cleanup is provided:
- on job close
- on process crash
- after node crash, on restart
- available space is re-checked for every forecast call (the native
component has to check again before writing)
Note: The 1st path that has enough space is chosen on job open (job
close/reopen triggers a new search)
67 lines
3.2 KiB
Plaintext
67 lines
3.2 KiB
Plaintext
[float]
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[[ml-forecasting]]
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=== Forecasting the Future
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After the {xpackml} features create baselines of normal behavior for your data,
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you can use that information to extrapolate future behavior.
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You can use a forecast to estimate a time series value at a specific future date.
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For example, you might want to determine how many users you can expect to visit
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your website next Sunday at 0900.
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You can also use it to estimate the probability of a time series value occurring
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at a future date. For example, you might want to determine how likely it is that
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your disk utilization will reach 100% before the end of next week.
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Each forecast has a unique ID, which you can use to distinguish between forecasts
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that you created at different times. You can create a forecast by using the
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{ref}/ml-forecast.html[Forecast Jobs API] or by using {kib}. For example:
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[role="screenshot"]
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image::images/ml-gs-job-forecast.jpg["Example screenshot from the Machine Learning Single Metric Viewer in Kibana"]
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//For a more detailed walk-through of {xpackml} features, see <<ml-getting-started>>.
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The yellow line in the chart represents the predicted data values. The
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shaded yellow area represents the bounds for the predicted values, which also
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gives an indication of the confidence of the predictions.
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When you create a forecast, you specify its _duration_, which indicates how far
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the forecast extends beyond the last record that was processed. By default, the
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duration is 1 day. Typically the farther into the future that you forecast, the
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lower the confidence levels become (that is to say, the bounds increase).
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Eventually if the confidence levels are too low, the forecast stops.
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You can also optionally specify when the forecast expires. By default, it
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expires in 14 days and is deleted automatically thereafter. You can specify a
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different expiration period by using the `expires_in` parameter in the
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{ref}/ml-forecast.html[Forecast Jobs API].
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//Add examples of forecast_request_stats and forecast documents?
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There are some limitations that affect your ability to create a forecast:
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* You can generate only three forecasts concurrently. There is no limit to the
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number of forecasts that you retain. Existing forecasts are not overwritten when
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you create new forecasts. Rather, they are automatically deleted when they expire.
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* If you use an `over_field_name` property in your job (that is to say, it's a
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_population job_), you cannot create a forecast.
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* If you use any of the following analytical functions in your job, you
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cannot create a forecast:
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** `lat_long`
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** `rare` and `freq_rare`
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** `time_of_day` and `time_of_week`
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+
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--
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For more information about any of these functions, see <<ml-functions>>.
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--
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* Forecasts run concurrently with real-time {ml} analysis. That is to say, {ml}
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analysis does not stop while forecasts are generated. Forecasts can have an
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impact on {ml} jobs, however, especially in terms of memory usage. For this
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reason, forecasts run only if the model memory status is acceptable.
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* The job must be open when you create a forecast. Otherwise, an error occurs.
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* If there is insufficient data to generate any meaningful predictions, an
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error occurs. In general, forecasts that are created early in the learning phase
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of the data analysis are less accurate.
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