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Practical Time Series Modeling and Analysis
--client
continuum
--show
pydata_sv_2013
--room a1 2235 --force
Next: 5 Scaling Machine Learning in Python
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Marks
Author(s):
Chang She
Location
A1
Date
mar Tue 19
Days Raw Files
Start
14:05
First Raw Start
14:04
Duration
00:50:00
Offset
0:00:18
End
14:55
Last Raw End
14:54
Chapters
00:00
0:00:07
Total cuts_time
44 min.
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Practical_Time_Series_Modeling_and_Analysis.json
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Exploratory analysis and predictive modeling of time series is an enormously important part of practical data analysis. From basic processing and cleaning to statistical modeling and analysis, Python has many powerful and high productivity tools for manipulating and exploring time series data using numpy, pandas, and statsmodels. We will use practical code examples to illustrate important topics such as: -resampling -handling of missing data -intraday data filtering -moving window computations -analysis of autocorrelation -predictive time series models -time series visualizations
Comment:
production notes
2013-03-19/14_04_42.dv
Apply:
14:06:37 - 14:06:44 ( 00:00:07 )
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E:
14:06:44
D:
00:02:02
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Start:
1:55)
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vlc ~/Videos/veyepar/continuum/pydata_sv_2013/dv/a1/2013-03-19/14_04_42.dv :start-time=0115.0 --audio-desync=0
Raw File
Cut List
14:04:42
seconds: 115.0
Wall: 14:06:37
Duration
00:02:02
14:06:44
seconds: 0.0
Wall: 14:04:42
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mp4
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2013-03-19/14_10_13.dv
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14:10:13 - 14:54:36 ( 00:44:23 )
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14:10:13 -
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14:54:36
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00:44:23
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vlc ~/Videos/veyepar/continuum/pydata_sv_2013/dv/a1/2013-03-19/14_10_13.dv :start-time=00.0 --audio-desync=0
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14:10:13
seconds: 0.0
Wall: 14:10:13
Duration
00:44:23
14:54:36
seconds: 0.0
Wall: 14:10:13
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mp4.m3u
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:
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2013-03-19/14_54_37.dv
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14:54:37 - 14:54:40 ( 00:00:03 )
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14:54:37 -
E:
14:54:40
D:
00:00:03
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vlc ~/Videos/veyepar/continuum/pydata_sv_2013/dv/a1/2013-03-19/14_54_37.dv :start-time=00.0 --audio-desync=0
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14:54:37
seconds: 0.0
Wall: 14:54:37
Duration
00:00:03
14:54:40
seconds: 0.0
Wall: 14:54:37
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2013-03-19/14_04_42.dv
2013-03-19/14_10_13.dv
2013-03-19/14_54_37.dv
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