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Possible Duplicate:
Simple algorithm for online outlier detection of a generic time seriesSimple algorithm for online outlier detection of a generic time series

How could I get rid of sparky data in a descrete data set, but in a "smoother out" manner?

Take for instance

http://i.stack.imgur.com/vb44Q.png

There are two sparks, at 20000, but the next one at 600 is also considered a spark.

I've managed to get the very high ones to zero, by

a = 2
b = 5
beta_dist = RealDistribution('beta', [a, b])
f(x) = x / 19968
normalized_insertions = [f(i) for i in insertions]

insertions_pairs = [(i, beta_dist.distribution_function(i)) for i in normalized_insertions]
plot_b = beta_dist.plot()

show(list_plot(insertions_pairs)+plot_b)

No idea how to go about the lower ones. The maximul should be reached at 100, perhaps the parameters for the beta distribution need a little more twiddling?

Currently, it looks like this:

http://i.stack.imgur.com/7hKHT.png

If possible, use sage as a reference for your explanations.

Possible Duplicate:
Simple algorithm for online outlier detection of a generic time series

How could I get rid of sparky data in a descrete data set, but in a "smoother out" manner?

Take for instance

http://i.stack.imgur.com/vb44Q.png

There are two sparks, at 20000, but the next one at 600 is also considered a spark.

I've managed to get the very high ones to zero, by

a = 2
b = 5
beta_dist = RealDistribution('beta', [a, b])
f(x) = x / 19968
normalized_insertions = [f(i) for i in insertions]

insertions_pairs = [(i, beta_dist.distribution_function(i)) for i in normalized_insertions]
plot_b = beta_dist.plot()

show(list_plot(insertions_pairs)+plot_b)

No idea how to go about the lower ones. The maximul should be reached at 100, perhaps the parameters for the beta distribution need a little more twiddling?

Currently, it looks like this:

http://i.stack.imgur.com/7hKHT.png

If possible, use sage as a reference for your explanations.

Possible Duplicate:
Simple algorithm for online outlier detection of a generic time series

How could I get rid of sparky data in a descrete data set, but in a "smoother out" manner?

Take for instance

http://i.stack.imgur.com/vb44Q.png

There are two sparks, at 20000, but the next one at 600 is also considered a spark.

I've managed to get the very high ones to zero, by

a = 2
b = 5
beta_dist = RealDistribution('beta', [a, b])
f(x) = x / 19968
normalized_insertions = [f(i) for i in insertions]

insertions_pairs = [(i, beta_dist.distribution_function(i)) for i in normalized_insertions]
plot_b = beta_dist.plot()

show(list_plot(insertions_pairs)+plot_b)

No idea how to go about the lower ones. The maximul should be reached at 100, perhaps the parameters for the beta distribution need a little more twiddling?

Currently, it looks like this:

http://i.stack.imgur.com/7hKHT.png

If possible, use sage as a reference for your explanations.

3 insert duplicate link
source | link

Possible Duplicate:
Simple algorithm for online outlier detection of a generic time series

How could I get rid of sparky data in a descrete data set, but in a "smoother out" manner?

Take for instance

http://i.stack.imgur.com/vb44Q.png

There are two sparks, at 20000, but the next one at 600 is also considered a spark.

I've managed to get the very high ones to zero, by

a = 2
b = 5
beta_dist = RealDistribution('beta', [a, b])
f(x) = x / 19968
normalized_insertions = [f(i) for i in insertions]

insertions_pairs = [(i, beta_dist.distribution_function(i)) for i in normalized_insertions]
plot_b = beta_dist.plot()

show(list_plot(insertions_pairs)+plot_b)

No idea how to go about the lower ones. The maximul should be reached at 100, perhaps the parameters for the beta distribution need a little more twiddling?

Currently, it looks like this:

http://i.stack.imgur.com/7hKHT.png

If possible, use sage as a reference for your explanations.

How could I get rid of sparky data in a descrete data set, but in a "smoother out" manner?

Take for instance

http://i.stack.imgur.com/vb44Q.png

There are two sparks, at 20000, but the next one at 600 is also considered a spark.

I've managed to get the very high ones to zero, by

a = 2
b = 5
beta_dist = RealDistribution('beta', [a, b])
f(x) = x / 19968
normalized_insertions = [f(i) for i in insertions]

insertions_pairs = [(i, beta_dist.distribution_function(i)) for i in normalized_insertions]
plot_b = beta_dist.plot()

show(list_plot(insertions_pairs)+plot_b)

No idea how to go about the lower ones. The maximul should be reached at 100, perhaps the parameters for the beta distribution need a little more twiddling?

Currently, it looks like this:

http://i.stack.imgur.com/7hKHT.png

If possible, use sage as a reference for your explanations.

Possible Duplicate:
Simple algorithm for online outlier detection of a generic time series

How could I get rid of sparky data in a descrete data set, but in a "smoother out" manner?

Take for instance

http://i.stack.imgur.com/vb44Q.png

There are two sparks, at 20000, but the next one at 600 is also considered a spark.

I've managed to get the very high ones to zero, by

a = 2
b = 5
beta_dist = RealDistribution('beta', [a, b])
f(x) = x / 19968
normalized_insertions = [f(i) for i in insertions]

insertions_pairs = [(i, beta_dist.distribution_function(i)) for i in normalized_insertions]
plot_b = beta_dist.plot()

show(list_plot(insertions_pairs)+plot_b)

No idea how to go about the lower ones. The maximul should be reached at 100, perhaps the parameters for the beta distribution need a little more twiddling?

Currently, it looks like this:

http://i.stack.imgur.com/7hKHT.png

If possible, use sage as a reference for your explanations.

    Post Closed as "exact duplicate" by whuber of
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Getting rid of sparksspikes in sample data

1
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