Decomposition of time series yields error

The frequency is really important and not arbitrary! So the decomposition needs to know when it is basically hitting the same "point" for a second time as it is treated as a "cycle". So in my example of the frequency of 4 we could say I measured every quarter once and therefore I would need at least 8 observations to have the minimum. If you think of it, I can have the 4 on top of the 4 and I will have a comparable rating for Q1 - 2019 vs Q1 - 2020 etc etc.

Thus, you need to specify a meaningful frequency. If I am looking at measuring my heart rate every hour and reporting it then my frequency would be 24 and I would need at least 24 x 2 = 48 to be able to decompose it. I could have had 70 "observations" which is not a perfect 3 days worth of measures but it still satisfies the 2 or more sets. Does it make more sense to you now?

On a side note, I can't exactly see how your data looks which makes this more difficult too. If you want me to look at it I suggest a reprex: FAQ: How to do a minimal reproducible example ( reprex ) for beginners