Data : I have a time series of temperature values every 5 minutes for 20 days. There is a common pattern across each day.

Question : What kind of pattern extraction or machine learning algorithm should be used to obtain a 'standard pattern / profile' of each day from this data ?

The idea is to compare deviations on future days from this 'standard pattern'.

I am unsure what should be the approach to solve this problem.

Sample daily temperatures are as follows: enter image description here

  • $\begingroup$ Maybe search this site for clustering of time series? And tell us a little more about your data $\endgroup$ – kjetil b halvorsen Apr 14 '18 at 13:00
  • $\begingroup$ Is there a long term trend in your data? Does the average daily temperature increase or decrease or is it stable? $\endgroup$ – Skander H. Apr 14 '18 at 15:04
  • $\begingroup$ You don't need machine learning here. A simple correlogram will work $\endgroup$ – Aksakal Apr 16 '18 at 21:44

Assuming you don't have any long term trend in your data, then you extract the daily pattern you are looking for by calculating the average over 20 days for each 5 minute interval. This would smooth out any day-to-day variances and show daily a pattern in your data (if there is one).

Consider a simple example with 3 days of data, measured at 4 hours intervals:

D1-4:00  D1-8:00    D1-12:00    D1-16:00    D1-20:00    D1-24:00
  4        11        15.5         17           8           6
D2-4:00  D2-8:00    D2-12:00    D2-16:00    D2-20:00    D2-24:00
  6        9          16          14          8.5          7
D3-4:00  D3-8:00    D3-12:00    D3-16:00    D3-20:00    D3-24:00. 
  5.5     9.5         14          14          14           2

Although some daily pattern is visible in this data, it is not very clear and there is a lot of daily variance. But if you calculate the average over 3 days for each two hour period:

4:00    8:00.   12:00  16:00   20:00   24:00
5.16    9.83    15.16    15    10.16     5

The pattern becomes much clearer.

You can do the same thing with your data, by averaging the value for every 5 minutes across 20 days.

If your data has a trend in it, then you need to detrend it first.


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