聚类您的时间序列数据

Question

I have time-series data of 12 consumers. The data corresponding to 12 consumers (named as a ... l ) is

I want to cluster these consumers so that I may know which of the consumers have utmost similar consumption behavior. Accordingly, I found clustering method pamk , which automatically calculates the number of clusters in input data.

I assume that I have only two options to calculate the distance between any two time-series, ie, Euclidean , and DTW . I tried both of them and I do get different clusters. Now the question is which one should I rely upon? and why?

When I use Eulidean distance I got following clusters:

and using DTW distance I got

Conclusion: How will you decide which clustering approach is the best in this case?

Note: I have asked the same question on Cross-Validated also.

Answer 1

1. none of the timeseries above look similar to me. Do you see any pattern? Maybe there is no pattern?

2. the clustering visualizations indicate that there are no clusters , too. b and l appear to be the most unusual outliers; followed by d,e,h ; but there are no clusters there.

3. Also try hierarchical clustering. The dendrogram may be more understandable.

But in either way, there may be no clusters . You need to be prepared for this outcome, and consider it a valid hypothesis. Double-check any result . As you have seen, pam will always return a result, and you have absolutely no means to decide which result is more "correct" than the other (most likely, neither is correct , and you should rely on neither, to answer your question).