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[DOCS] Updates changelog for 7.0.0-alpha2 #347

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Dec 18, 2018
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[DOCS] Updates changelog for 7.0.0-alpha2 #347

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[DOCS] Updates changelog for 7.0.0-alpha2
lcawl Dec 17, 2018
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[DOCS] Cleans up PR list
lcawl Dec 18, 2018
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89 changes: 5 additions & 84 deletions docs/CHANGELOG.asciidoc
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//=== Regressions

== {es} version 6.6.0

=== Breaking Changes

=== Deprecations

=== New Features

=== Enhancements

=== Bug Fixes

Fix cause of "Sample out of bounds" error message (See {ml-pull}355[355].}

=== Regressions

== {es} version 6.5.3

=== Bug Fixes

Correct query times for model plot and forecast in the bucket to match the times we assign
the samples we add to the model for each bucket. For long bucket lengths, this could result
in apparently shifted model plot with respect to the data and increased errors in forecasts.

== {es} version 6.5.0

//=== Breaking Changes

//=== Deprecations

//=== New Features

=== Enhancements

Perform anomaly detection on features derived from multiple bucket values to improve robustness
of detection with respect to misconfigured bucket lengths and improve detection of long lasting
anomalies. (See {ml-pull}175[#175].)

Support decomposing a time series into a piecewise linear trend and with piecewise constant
scaling of the periodic components. This extends our decomposition functionality to handle the
same types of change points that our modelling capabilities do. (See {ml-pull}198[198].)

Increased independence of anomaly scores across partitions (See {ml-pull}182[182].)

Avoid potential false positives at model start up when first detecting new components of the time
series decomposition. (See {ml-pull}218[218].)

Add a new label - multi_bucket_impact - to record level anomaly results.
The value will be on a scale of -5 to +5 where -5 means the anomaly is purely single bucket
and +5 means the anomaly is purely multi bucket. ({ml-pull}230[230])

Improve our ability to detect change points in the presence of outliers. (See {ml-pull}265[265].)
== {es} version 7.0.0-alpha2

=== Bug Fixes

Fix cause of "Bad density value..." log errors whilst forecasting. ({ml-pull}207[207])

Fix incorrectly missing influencers when the influence field is one of the detector's partitioning
fields and the bucket is empty. ({pull}219[#219])

Fix cause of hard_limit memory error for jobs with bucket span greater than one day. ({ml-pull}243[243])

Fix cause of "Failed to compute significance..." log errors ({ml-pull}272[272])"

Prevent detecting a trend component during a possible change in the time series. The resulting
model was poorly reinitialised in this case which damaged anomaly detection for some time. (See
{ml-pull}287[#287].)

Fix cause of "MERGE: Sum mode samples = 0, total samples = 4.43521.." log errors ({ml-pull}294[294])

//=== Regressions
* Fixes CPoissonMeanConjugate sampling error. {ml-pull}335[#335]
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It's true that this isn't currently in a GA version, but it will be in 6.6. So when the eventual 7.0.0 GA release notes are created it shouldn't be in there. Might it be worth adding a comment here to say it should be removed when consolidating into the GA 7.0.0 release notes?

//NOTE: Remove from final 7.0.0 release notes if already in 6.x

== {es} version 6.4.3

//=== Breaking Changes

//=== Deprecations

//=== New Features
== {es} version 7.0.0-alpha1

=== Enhancements

Change linker options on macOS to allow Homebrew installs ({ml-pull}225[225])

//=== Bug Fixes

Rules that trigger the `skip_model_update` action should also apply to the anomaly model.
This fixes an issue where anomaly scores of results that triggered the rule would decrease
if they occurred frequently. (See {ml-pull}222[#222].)

//=== Regressions
* Adds include categorical filter type to detector rules. {ml-pull}27[#27]