I'm making a project connected with identifying dynamic of sales. That's how the piece of my database looks like:
There are free columns:
- Product - present the group of product
- Week - time since launch the product (week), first 26 weeks
- Sales_gain - how the sales of product change by weeks
In the database there is 3302 observations = 127 time series
My aim is to cluster time series in groups which are going to show me different dynamic of sales. Before clustering I want to use Fast Fourier Transform to change time series on vectors and take into consideration amplitude etc and then use a distance algorithm and group products.
It's my first time I deal with FFT and clustering, so I would be grateful if anybody would point steps, which I have to do before/after using FFT to group dynamics of sales. I want to do all steps in R, so it would be wonderful if somebody type which procedures should I use to do all steps.
Thats how my time series looks like now:
Please note that I am relatively new to time series analysis (that's why I cannot put here my code) so any clarity you could provide in R or any package you could recommend that would accomplish this task efficiently would be appreciated.
P.S. Instead of FFT I found the code for DTW here -> link
but cannot use it on my data base and time series (suggest R to analyze new time series after 26 weeks). Can somebody explain it to me?