Scaling forecasting algorithms using clustered modeling

Abstract

Research on forecasting has traditionally focused on building more accurate statistical models for a given time series. The models are mostly applied to limited data due to efficiency and scalability problems. However, many enterprise applications require scalable forecasting on large number of data series. For example, telecommunication companies need to forecast each of their customers’ traffic load to understand their usage behavior and to tailor targeted campaigns. Forecasting models are typically applied on aggregate data to estimate the total traffic volume for revenue estimation and resource planning. However, they cannot be easily applied to each user individually as building accurate models for large number of users would be time consuming. The problem is exacerbated when the forecasting process is continuous and the models need to be updated periodically. This paper addresses the problem of building and updating forecasting models continuously for multiple data series. We propose dynamic clustered modeling for forecasting by utilizing representative models as an analogy to cluster centers. We apply the models to each individual series through iterative nonlinear optimization. We develop two approaches: The Integrated Clustered Modeling integrates clustering and modeling simultaneously, and the Sequential Clustered Modeling applies them sequentially. Our findings indicate that modeling an individual’s behavior using its segment can be more scalable and accurate than the individual model itself. The grouped models avoid overfits and capture common motifs even on noisy data. Experimental results from a telco CRM application show the method is efficient and scalable, and also more accurate than having separate individual models.