<ul>
<li><strong>Cross correlation</strong>
A high cross-correlation value indicates a strong correlation between the two signals, which can be useful for identifying the specific event that one of the signals is sensitive to.</li>
<li><strong>TimeLag</strong>
Which represents the time delay between the occurrence of the event in one signal and its effect on the other signal.</li>
<li><strong>Frequency</strong> <strong>Analysis</strong>
This can be useful for identifying any periodicities or cycles in the signals that may be related to the specific event.</li>
<li><strong>Statistical</strong> <strong>Analysis</strong>
You can perform various statistical analyses of the two signals, such as mean, median, variance, and standard deviation, to compare their overall characteristics.</li>
</ul>

CC BY-SA 4.0
2023-04-17T09:57:14.750Z
2023-04-17T09:57:14.750Z
Joypal
<p>i have two temperature signals from which one is sensitive toward a specific event. I would like to know what other features can be useful to extract apart from:</p>
<ol>
<li>Angles (between the two).</li>
<li>Slopes ( between the two).</li>
</ol>
<p>P.S. Language is Python</p>

<python><time-series><feature-extraction><feature-engineering>
Feature Extraction for timeseries temperature signal
Mirza
# Feature Extraction for timeseries temperature signal

(Asked by: username_0 on 2023-04-17)

i have two temperature signals from which one is sensitive toward a specific event. I would like to know what other features can be useful to extract apart from:

1. Angles (between the two).
2. Slopes ( between the two).

P.S. Language is Python

**Tags:** python, time-series, feature-extraction, feature-engineering


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## 1 answers

### Answer by username_1 on 2023-04-17. Score: 1

* **Cross correlation**
  A high cross-correlation value indicates a strong correlation between the two signals, which can be useful for identifying the specific event that one of the signals is sensitive to.
* **TimeLag**
  Which represents the time delay between the occurrence of the event in one signal and its effect on the other signal.
* **Frequency** **Analysis**
  This can be useful for identifying any periodicities or cycles in the signals that may be related to the specific event.
* **Statistical** **Analysis**
  You can perform various statistical analyses of the two signals, such as mean, median, variance, and standard deviation, to compare their overall characteristics.


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