I'd like to cluster some time series that describe a flow of a variable (say, temperature) throughout a day. Measurements are made every 5 minutes so each time series has 288 values.
Are we talking of high-dimensionality in this case? Will the euclidean distance perform well if I'd like to cluster my data with k-means? Should I find a way to reduce the dimensionality?
More specifically, does a high-dimensionality of a time series refer to its length, number of variables or both?
There are some other topics that touch this topic but there is no direct answer: yes - no. There is also a quite similar question on the Cross Validated but unfortunately it hasn't been answered: Curse of dimensionality for time series?