Fuzzy c-auto regression models

Fuzzy c-auto regression models (FCARM) combine clustering with time series prediction. Given a set of time series, FCARM finds clusters of time series with similar dynamics. More specifically, FCARM finds a partition matrix that quantifies to which degree each time series is associated with each prediction model, and the parameters of the (linear) auto regression models for each cluster. FCARM can thus be used for two different purposes: (i) the automatic identification of clusters of time series with similar dynamics and (ii) the forecast of a large number of time series using only a small number of generic forecast models, leading to higher data efficiency and lower model validation and maintenance effort. We illustrate the application of FCARM to sales forecasts for products that can be clustered into groups with similar sales dynamics.