I have the simple multivariate time series looks like this,
Each column could be plotted in line chart as below,
My problem statement is about is there any algorithm or machine learning to prioritize three columns based on data or charts weekly? Since each of column represents a metric(A metric, B metric and C metric), I would like to know the importance based on data without any domain knowledge and given the ranking to each one. Like feature importance but without corresponding responses. For example, A-1, B-3 and C-2.
My current possible thoughts would be,
- Calculate the slope from entering and ending points weekly, and given rates from 1 to end.
- If encountering any same values, cosine similarity determines whether two vectors are the same or not. Meaning if similarity is close to 1, two vectors are the same.
However, there are some problems using this method,
- If similarity is close to 0, having no other thoughts to tell which vector is more important to another.
- No metric to evaluate this method is a good fit or not.
- Not robust ways to solve this problem...
Any discussion would be appreciated as well! Thanks!
8/27- Corrected grammar New item, the background of data
The data is not generated form any video or sensor. It just happen to denote as time series data and the frequency of data are quite irregular. The data shown as above are aggregated afterwards. The characteristics of each univariate data is that there are many 0 values (i.e. sparse data). Size of original data is not quite large, usually 10 or less points per days. When I am talking about structured data is because the data source is not from any video or sensor. It more like a mark down over time. Take the 1st row as an example, each column represents how much time spending on item, A ,B and C on each date.