Let's suppose we have a series of time-based data describing a dynamic (e.g., air temperature over the year, measured once a day). Is it possible to apply a statistical technique that allows me to identify similar periods? An example of the expected results could be: 1) January-March as one group, then 2) April-June, 3) July-October, and 4) October-December
Usually, I don't deal with data that don't stem from experimental designs or similar ;-)
Seems like you have a data set with time series format. You can use r studio with mutate, filter and or select functions to choose the data with specific dates or months. I will be able to help more after taking a look at the data structure.
Hope this helps.
Thanks
Let's say I have daily weather data (air temperature, wind, precipitation, etc.) for an entire year. Beyond the software, can I use, for example, K-means to cluster similar periods? Let's say, for example, that I want to identify 3 different periods within the entire year.
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