I have 5 pieces of time series data. It is the weekly sales of 5 different stores (A,B,C,D,E). There are no missing values. A quick visual inspection shows that these 5 pieces of time series data have similar trend & seasonality.
I would like to calculate/quantify how similar Store B,C,D,E is to Store A respectively.
I know how to calculate the simple cosine distance and Euclidean distance, and I have experience dealing with time series data (e.g. ARIMA, Prophet), but never had to deal with finding (i.e. quantifying) the similarity / distance between time series data.
Some google search suggests that "dynamic time warping" could be the method to go. I have not yet heard of this algorithm before, so not sure if this is indeed the right direction.
Additionally, can I still perform cosine distance and Euclidean distance on time series data? Is that doable?
How do I approach this issue? Any insights are appreciated.