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I am trying to cluster dozens of time-series sampled every 30min, and which cover the period mid2016 - mid2020. Most of them have very nice "patterns", others may have missing values for a given period (eg: one whole year, severals months, etc) or be more "chaotic" (sudden variations).

Here I display some of the time-series I am handling: enter image description here

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If we look at a closer level (eg: weekly), it is possible to see some seasonal patterns as the graphs below show (2020/1/1 to 2020/1/8):

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Ideally, I would like to make clusters where time-series share similar "shapes in time" (eg: similar shape based on time --> peaks on the morning and evening, almost null values on weekends or holidays, etc) but also, if possible, yearly seasonality when enough data are available.

I tried to apply the commonly used DTW measure + hierarchical clustering (ward linkage), but because of the number of points I have per time-series (even after doing 1hr resampling), it took too much time and I was quite disappointed with the results (though I applied on data with few amount of preprocessing).

So what I am facing is:

  • I would like to extract the "nicest" part of each time series, but if I do so, they will be misaligned (do not start at the same time point) and they will be of different length. Thus, I am quite confused to the preprocessing steps I should employ.

I would be glad if you have some advice about preprocessing / distance / clustering algorithm that I should I apply to perform clustering of these time series.

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Preprocessing that results in misalignment or different lengths is not necessarily a problem. Have you considered Time Series Clustering - a decade review (Information Systems 53, 2015), in which Aghabozorgi et al review 38 algorithms for clustering whole time-series? See the rightmost column of Table 4 (pages 27-28) for their notes describing attributes of each of the approaches.

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  • $\begingroup$ Thank you for your answer ! The review you mentioned is the one I am using to try finding a proper way to clusterize my time series. Do you agree that my problem is categorized as what they call “Finding similar time series in time” (p23)? In Table 4, what should I take care of ? “Noise robustness” in case I use my raw series? Is it really ok to use my series as they are or should I extract “nicest” part of them before applying clustering (eg taking the few latest months as they are the most “stable”) ? Thank you $\endgroup$ – yoyoog Jul 27 at 14:34

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