DTW (dynamic time-warping) is good when you don't know when the event starts and how they align. Say you are capturing heart measurements. You cannot expect to have the hearts of two subjects to beat in synchronization. DTW will try to align this data, and may (or may not) succeed in matching e.g. an anomaly in the heart beat of two subjects. In theory...
FFT and DWT are good when your data will have interesting repetitive patterns, and you have A) a good temporal resolution (for audio data, e.g. 16000 Hz - I am talking about thousands of data points!) and B) you have no idea of what frequencies to expect. If you know e.g. you will have weekly patterns (e.g. no sales on sundays) then you should filter them with other algorithms instead.
I suggest to do a lot of preprocessing, than whatever algorithm you feel comfortable with, and which yields reasonable results. But preprocessing is key. (And depends on your data, we cannot help you preprocess).
Maybe all you need is spend more time in preprocessing your data, in particular normalization, to be able to capture similarity.
Thanks for your answer! so what method do you suggest? The result, I want to achieve, is to cluster products with different dynamic of sales and present these different dynamics on plots.
You may have too little data for FFT/DWT to make sense. DTW may be better, but I also don't think it makes sense for sales data - why would there be a x-week temporal offset from one location to another? It's not as if the data were captured at unknown starting weeks.