How would you incorporate the temporal pattern as a predictor in your model, where the unit of analysis is the customer? You can achieve this by categorizing each of the original series and using the category labels as inputs in your predictive model: For example, suppose you have a hunch that customer’s behavior over time would help predict churn or fraud. ![]() TSC can also help you incorporate time series in traditional data mining applications such as customer churn prediction and fraud identification. Time Series Clustering (TSC) can be used to find stocks that behave in a similar way, products with similar sales cycles, or regions with similar temperature profiles. ![]() When you work with data measured over time, it is sometimes useful to group the time series.
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