Abstract: Techniques are described for generating characterizations of time series data. In one example, a method includes extracting a trend-cycle component, a seasonal component, and an irregular component from a time series of data. The method further includes performing one or more pattern analyses on the trend-cycle component, the seasonal component, and the irregular component. The method further includes, for each pattern analysis of the one or more pattern analyses, performing a comparison of an analytic result of the respective pattern analysis to a selected significance threshold for the respective pattern analysis to determine if the analytic result passes the significance threshold for the respective pattern analysis. The method further includes generating an output for each of the analytic results that pass the significance threshold for the respective pattern analysis.

Abstract: In one example, a method includes determining, based on historical purchase data for a customer, an expectancy value that indicates when the customer is expected to make a purchase from a business, determining, based on the historical purchase data for the customer, a frequency value that indicates at what frequency the customer is expected to make purchases from the business during a future time period, and determining, based on the historical purchase data for the customer, a monetary value that indicates how much the customer is expected to spend during the future time period. In this example, the method includes determining, based on the expectancy value, the frequency value, and the monetary value, a future customer value score that indicates how valuable the customer is likely to be in the future time period.

Abstract: In one example, a method includes determining, based on historical purchase data for a customer, an expectancy value that indicates when the customer is expected to make a purchase from a business, determining, based on the historical purchase data for the customer, a frequency value that indicates at what frequency the customer is expected to make purchases from the business during a future time period, and determining, based on the historical purchase data for the customer, a monetary value that indicates how much the customer is expected to spend during the future time period. In this example, the method includes determining, based on the expectancy value, the frequency value, and the monetary value, a future customer value score that indicates how valuable the customer is likely to be in the future time period.