Possible features for the data generated by array of gas sensors
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The data is generated by an array of gas sensors to classify the respective gas odor. I am attaching the snippet of the data.
How can I derive the following features for the data in python?
Change over time: Cm+1 = (Xm+1 – Xm)/(tm+1 – tm)
Rate of change over time: RTm= (Cm+1 – Cm)/(tm+1 – tm)
Growth or decay: Gm+1 = (Xm+1 – Xm)/Xm
Rate of growth or decay: RGm= (Gm+1 – Gm)/(tm+1 – tm)
Count of values above or below a threshold value
Moving average = Average of (Xm-p to Xm)
Moving standard deviation = Standard deviation of (Xm-p to Xm)
Relative average = Moving average / Global average
Relatives standard deviation = Moving standard deviation / Global standard deviation
Ratio of changes, growth rate etc. with standard deviation
Features involving trend of values across various aggregation windows: change and rate of change in average, standard deviation etc. across windows
Any demonstration will be highly helpful for my understanding. Thanks in advance.
python
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$begingroup$
The data is generated by an array of gas sensors to classify the respective gas odor. I am attaching the snippet of the data.
How can I derive the following features for the data in python?
Change over time: Cm+1 = (Xm+1 – Xm)/(tm+1 – tm)
Rate of change over time: RTm= (Cm+1 – Cm)/(tm+1 – tm)
Growth or decay: Gm+1 = (Xm+1 – Xm)/Xm
Rate of growth or decay: RGm= (Gm+1 – Gm)/(tm+1 – tm)
Count of values above or below a threshold value
Moving average = Average of (Xm-p to Xm)
Moving standard deviation = Standard deviation of (Xm-p to Xm)
Relative average = Moving average / Global average
Relatives standard deviation = Moving standard deviation / Global standard deviation
Ratio of changes, growth rate etc. with standard deviation
Features involving trend of values across various aggregation windows: change and rate of change in average, standard deviation etc. across windows
Any demonstration will be highly helpful for my understanding. Thanks in advance.
python
asked 5 mins ago
achach
1
New contributor
ach is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
The data is generated by an array of gas sensors to classify the respective gas odor. I am attaching the snippet of the data.
How can I derive the following features for the data in python?
Change over time: Cm+1 = (Xm+1 – Xm)/(tm+1 – tm)
Rate of change over time: RTm= (Cm+1 – Cm)/(tm+1 – tm)
Growth or decay: Gm+1 = (Xm+1 – Xm)/Xm
Rate of growth or decay: RGm= (Gm+1 – Gm)/(tm+1 – tm)
Count of values above or below a threshold value
Moving average = Average of (Xm-p to Xm)
Moving standard deviation = Standard deviation of (Xm-p to Xm)
Relative average = Moving average / Global average
Relatives standard deviation = Moving standard deviation / Global standard deviation
Ratio of changes, growth rate etc. with standard deviation
Features involving trend of values across various aggregation windows: change and rate of change in average, standard deviation etc. across windows
Any demonstration will be highly helpful for my understanding. Thanks in advance.
python
asked 5 mins ago
achach
1
New contributor
ach is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
The data is generated by an array of gas sensors to classify the respective gas odor. I am attaching the snippet of the data.
How can I derive the following features for the data in python?
Change over time: Cm+1 = (Xm+1 – Xm)/(tm+1 – tm)
Rate of change over time: RTm= (Cm+1 – Cm)/(tm+1 – tm)
Growth or decay: Gm+1 = (Xm+1 – Xm)/Xm
Rate of growth or decay: RGm= (Gm+1 – Gm)/(tm+1 – tm)
Count of values above or below a threshold value
Moving average = Average of (Xm-p to Xm)
Moving standard deviation = Standard deviation of (Xm-p to Xm)
Relative average = Moving average / Global average
Relatives standard deviation = Moving standard deviation / Global standard deviation
Ratio of changes, growth rate etc. with standard deviation
Features involving trend of values across various aggregation windows: change and rate of change in average, standard deviation etc. across windows
Any demonstration will be highly helpful for my understanding. Thanks in advance.
python
python
asked 5 mins ago
achach
1
New contributor
ach is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
achach
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New contributor
ach is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
achach
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New contributor
ach is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
achach
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asked 5 mins ago
achach
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1
New contributor
ach is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
ach is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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