DATA MINING APPROACH TO

FORECASTING TELECOMMUNICATIONS

DEMANDS AND NETWORK LOADS

Many telecommunications companies are placing an increasingly high economic value on the ability to accurately forecast demand for local loop and access products, ranging from primary residential lines (1FRs) to wireless, data, and bundled local/long-distance services, over a planning horizon of several years. Failure to forecast demand growth with sufficient accuracy has recently led many experienced providers to inadequate capacity provisioning, held orders, and sometimes regulatory penalties or churn of customers away to competitors. In response to the need for more accurate demand and network load forecasts, Cox Associates has developed and successfully applied a new approach to demand forecasting that can radically improve forecast accuracy compared to even the most sophisticated time series methods.  
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DEMAND FORECASTING AND CAPACITY PLANNING Failure to forecast demand growth with sufficient accuracy has recently led many experienced providers to inadequate capacity provisioning, held orders, and sometimes regulatory penalties or churn of customers away from competitors.

How Classification

Trees Work

Two pragmatic complexities have previously prevented straight-forward implementation of this strategy. They are: a.The almost infinite number of ways to partition the units of analysis into groups. Identifying groupings that minimize forecast errors has been a continuing challenge for marketers and statisticians. b.Difficulties in predicting the flow of units among the different groups over time. Our technique provides constructive solutions to both problems, as follows. 1.Individuals are automatically partitioned into groups (defined by conjunctions of attribute values) to minimize forecast errors. This partitioning is accomplished via the data-mining and artificial intelligence technique of classification tree analysis. The quantities forecast -- product acquisition, penetration, usage, and retention/attrition/churn rates -- are modeled as transition rates in a multi-state, semi- Markov process with covariates that include demographic, billing, and usage history information. Transition rates are estimated as functions of these covariates via the classification tree algorithm. 2.Transitions of individual units among groups are modeled by applying the transition rates estimated in part (a) to the groups. This determines the evolution of the distribution of individuals among groups over time. A dynamic simulation model is used to integrate the transition rate and covariate information and to predict the resulting changes in product demands over time.
Technical References