[DOCS] Fix movavg images and naming
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@ -1,5 +1,5 @@
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[[search-aggregations-reducer]]
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include::reducer/derivative.asciidoc[]
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include::reducer/derivative-aggregation.asciidoc[]
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include::reducer/max-bucket-aggregation.asciidoc[]
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include::reducer/movavg-reducer.asciidoc[]
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include::reducer/movavg-aggregation.asciidoc[]
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@ -132,14 +132,14 @@ track the data and only smooth out small scale fluctuations:
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[[movavg_10window]]
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.Moving average with window of size 10
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image::images/movavg_10window.png[]
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image::images/reducers_movavg/movavg_10window.png[]
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In contrast, a `simple` moving average with larger window (`"window": 100`) will smooth out all higher-frequency fluctuations,
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leaving only low-frequency, long term trends. It also tends to "lag" behind the actual data by a substantial amount:
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[[movavg_100window]]
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.Moving average with window of size 100
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image::images/movavg_100window.png[]
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image::images/reducers_movavg/movavg_100window.png[]
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==== Linear
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@ -166,7 +166,7 @@ will closely track the data and only smooth out small scale fluctuations:
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[[linear_10window]]
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.Linear moving average with window of size 10
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image::images/linear_10window.png[]
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image::images/reducers_movavg/linear_10window.png[]
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In contrast, a `linear` moving average with larger window (`"window": 100`) will smooth out all higher-frequency fluctuations,
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leaving only low-frequency, long term trends. It also tends to "lag" behind the actual data by a substantial amount,
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@ -174,7 +174,7 @@ although typically less than the `simple` model:
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[[linear_100window]]
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.Linear moving average with window of size 100
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image::images/linear_100window.png[]
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image::images/reducers_movavg/linear_100window.png[]
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==== Single Exponential
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@ -204,11 +204,11 @@ The default value of `alpha` is `0.5`, and the setting accepts any float from 0-
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[[single_0.2alpha]]
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.Single Exponential moving average with window of size 10, alpha = 0.2
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image::images/single_0.2alpha.png[]
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image::images/reducers_movavg/single_0.2alpha.png[]
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[[single_0.7alpha]]
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.Single Exponential moving average with window of size 10, alpha = 0.7
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image::images/single_0.7alpha.png[]
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image::images/reducers_movavg/single_0.7alpha.png[]
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==== Double Exponential
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@ -247,11 +247,11 @@ values emphasize short-term trends. This will become more apparently when you a
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[[double_0.2beta]]
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.Double Exponential moving average with window of size 100, alpha = 0.5, beta = 0.2
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image::images/double_0.2beta.png[]
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image::images/reducers_movavg/double_0.2beta.png[]
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[[double_0.7beta]]
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.Double Exponential moving average with window of size 100, alpha = 0.5, beta = 0.7
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image::images/double_0.7beta.png[]
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image::images/reducers_movavg/double_0.7beta.png[]
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=== Prediction
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@ -279,7 +279,7 @@ of the last value in the series, producing a flat:
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[[simple_prediction]]
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.Simple moving average with window of size 10, predict = 50
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image::images/simple_prediction.png[]
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image::images/reducers_movavg/simple_prediction.png[]
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In contrast, the `double_exp` model can extrapolate based on local or global constant trends. If we set a high `beta`
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value, we can extrapolate based on local constant trends (in this case the predictions head down, because the data at the end
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@ -287,11 +287,11 @@ of the series was heading in a downward direction):
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[[double_prediction_local]]
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.Double Exponential moving average with window of size 100, predict = 20, alpha = 0.5, beta = 0.8
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image::images/double_prediction_local.png[]
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image::images/reducers_movavg/double_prediction_local.png[]
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In contrast, if we choose a small `beta`, the predictions are based on the global constant trend. In this series, the
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global trend is slightly positive, so the prediction makes a sharp u-turn and begins a positive slope:
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[[double_prediction_global]]
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.Double Exponential moving average with window of size 100, predict = 20, alpha = 0.5, beta = 0.1
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image::images/double_prediction_global.png[]
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image::images/reducers_movavg/double_prediction_global.png[]
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