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:
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.
