wind time series

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WIND time series

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WIND time series. WIND time series cont. WIND time series cont. CH plan for WIND data, no delay. No delay cont. WIND data- Normalized Permutation Entropies. WIND data- Jensen-Shannon Complexities. CH plan for WIND data, standard delay 10. D elay 10 cont. - PowerPoint PPT Presentation

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Page 1: WIND time series

WIND time series

Page 2: WIND time series

WIND time series cont.

Page 3: WIND time series

WIND time series cont.

Page 4: WIND time series

CH plan for WIND data, no delay

Page 5: WIND time series

Fast Stream Slow Stream CME

B_x 0.929 0.917 0.918

B_y 0.929 0.921 0.924

B_y 0.928 0.920 0.922

Fast Stream Slow Stream CME

B_x 0.102 0.118 0.117

B_y 0.102 0.112 0.108

B_y 0.102 0.113 0.110

WIND data- Normalized Permutation Entropies

WIND data- Jensen-Shannon Complexities

No delay cont.

Page 6: WIND time series

CH plan for WIND data, standard delay 10

Page 7: WIND time series

Fast Stream Slow Stream CME

B_x 0.965 0.963 0.964

B_y 0.961 0.960 0.963

B_y 0.960 0.959 0.962

Fast Stream Slow Stream CME

B_x 0.055 0.056 0.056

B_y 0.059 0.060 0.056

B_y 0.060 0.062 0.057

WIND data- Normalized Permutation Entropies

WIND data- Jensen-Shannon Complexities

Delay 10 cont.

Page 8: WIND time series

WIND complexities and entropies as functions of τ

Page 9: WIND time series

WIND complexities and entropies as functions of τ

Page 10: WIND time series

WIND complexities and entropies as functions of τ

Page 11: WIND time series

WIND complexities and entropies as functions of τ

Page 12: WIND time series

WIND complexities and entropies as functions of τ

Page 13: WIND time series

WIND complexities and entropies as functions of τ

Page 14: WIND time series

WIND complexities and entropies as functions of τ

Page 15: WIND time series

WIND complexities and entropies as functions of τ

Page 16: WIND time series

WIND complexities and entropies as functions of τ

Page 17: WIND time series

• The next 5 slides show the results of analysis run on

5000-value portions of the CME time series

corresponding to an event and a more stationary period.

• Without any filtering, there is no definitive trend across

spatial directions marking one portion as more

stochastic (lower C, higher PE) than the other.

• When an embedding delay of 10 is used, the stationary

and event signals to separate as expected for all spatial

directions, with the event slightly more complex and

less entropic. Still, the difference is quite small.

Page 18: WIND time series

CME Event

Page 19: WIND time series

CME Stationary Period

Page 20: WIND time series

CH plan for CME event and stationary portions, no delay

Page 21: WIND time series

CH plan for CME event and stationary portions, delay 10

Page 22: WIND time series

Normalized PE

Event Stationary

B_x 0.918 0.902

B_y 0.915 0.924

B_z 0.910 0.912

Complexity

B_x 0.116 0.140

B_y 0.121 0.111

B_z 0.127 0.124

Normalized PE

Event Stationary

B_x 0.917 0.952

B_y 0.917 0.950

B_z 0.931 0.946

Complexity

B_x 0.130 0.085

B_y 0.133 0.089

B_z 0.110 0.094

Unfiltered

Delay 10

Page 23: WIND time series

CH plane with positions of 10 sections of B_x fast stream time series

Standard Deviation in PE = 0.006

Standard Deviation in C = 0.007