I'm new to data science and I'm currently working on a project to classify electricity consumption profiles.

This consists of electricity meter readings taken from sites on a half-hourly interval over a year (17520 readings in total). I am using Python to analyse the data.

I have settled on the current process of z-normalising the data, applying dynamic time warping on the entire dataset (using DTAIDistance) and then using agglomerative clustering as suggested by answers to similar questions on the site. I have produced the dendrogram below using 'complete' linkage using scipy.


I have some questions regarding the process.

  1. What is the "best"* metric to determine the quality of clustering (i.e. where do I set the cut off on my dendrogram?) and is there any iterative method to determine the "optimal" cut off? (* I am aware that "best" is quite subjective for this type of clustering)
  2. Following up on 1. - say if I want to produce an elbow plot from my DTW distance matrix. How would I go about doing this in Python?
  3. How can I find out if I have selected the correct linkage method? I have discounted Ward linkage as it apparently relies on an Euclidean distance matrix and have plotted dendrogram using other linkage methods, but I am unsure as to assess their suitability for my dataset.
  4. I have read that z-normalising data is required for most applications of DTW - when is z-normalising not required for DTW?
  5. Are any other clustering methods I should consider?

Visually, there are no clusters in your data.

If your data had clusters, there would be subtrees with a small MRCA, with a long line leading to it.

There are metrics to quantity this, but you are not there yet. Can you show the time series? (or better, send them to me)

You may be lucky, and have one of ten problems that are easy to fix. See this paper.

You ask " when is z-normalising not required for DTW?" The answer is never. eamonn


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