I have a data set (24 x 1000) (hour x kwh) which contains 1000 time series of a buildings' power consumption, measured every hour. After applying k-means clustering using the dtw criterion I create 5 clusters as shown in the image below.
For a new day I am starting to collect values for the incoming time series. Hour 0 = 1.8 kwh, Hour 1 = 0.6 kwh, etc.
I want to create a model that will give me an indication from hour 0. That indication will show me how likely it is the incoming partial time series to belong in each cluster and it will change for every hour that I have a new incoming value. How would you approach this problem? If my descriptions is vague, please ask me anything. I am thinking a probabilistic solution...