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I have a retail store dataset, and I am interested to do some time series clustering on this data, what idea you find interesting for this purpose?

I have so far:

  • What sales trends there are across time?
  • What products customers will purchase at what time?
  • Customer segmentation across time?

Any better ideas?

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    $\begingroup$ Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. $\endgroup$
    – Community Bot
    May 20 at 6:05

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Time series could be applied for sales in various different fields. Here a quite extended view. There is the ones you've mentionned:

  • What sales trends there are across time.
  • What products customers will purchase at what time (day/week/month/year seasonality).
  • Customer segmentation across time.

And also other ones:

  • Sales predictions based on stock markets correlations (raw materials,etc.).
  • Sales predictions based on other events such as weather, celebration day, virus, scarcity and scarcity threats, inflation, etc. (can be found in news data source like twitter)
  • Correlation study between items in space or in time (ex: sales of a product A could lead later to sales of a product B).

If available:

  • Current customer profiles/population studies to make marketing predictions.
  • Historical demographic data to evaluate changes and trends (purchasing power, beliefs,etc.)
  • Items organisation. Some sales can be improved by puting related items next to each other (ex: sun glasses and ice cream).
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  • $\begingroup$ Thank you, Given that I I have a retail dataset, and II I want to do time series clustering, and III I want to answer this question: which products correlated/clustered with each other across time; I have some follow-up questions if you can address please: 1) Which algorithms are the fastest and suitable: Partitional (kmeans), density based (DBSCAN or HDBSCAN with hierarchical), hierarchical (agglomerative)? 2) Which distance measure would be suitable; correlation distance, DTW, or some other measure (I am not including Euclidean)? $\endgroup$ May 20 at 18:33
  • $\begingroup$ You have plenty of options to cluster data interestingly. I use to suggest a dimensional reduction algorithm (like t-SNE or UMAP) because it clusterizes data relatively well. Then, you can also apply the algorithms you've mentionned or even random forest: it is usually a good option. In addition to that, XGBoost could be even better (see towardsdatascience.com/…). $\endgroup$ May 21 at 7:19
  • $\begingroup$ datascience.stackexchange.com/questions/111277/… $\endgroup$ May 25 at 0:13

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