Ecosystem on the Web: non-linear mining and forecasting of co-evolving online activities

Given a large collection of co-evolving online activities, such as searches for the keywords “Xbox”, “PlayStation” and “Wii”, how can we find patterns and rules? Are these keywords related? If so, are they competing against each other? Can we forecast the volume of user activity for the coming month? We conjecture that online activities compete for user attention in the same way that species in an ecosystem compete for food. We present EcoWeb , (i.e., Ecosystem on the Web), which is an intuitive model designed as a non-linear dynamical system for mining large-scale co-evolving online activities. Our second contribution is a novel, parameter-free , and scalable fitting algorithm, EcoWeb - Fit , that estimates the parameters of EcoWeb . Extensive experiments on real data show that EcoWeb is effective , in that it can capture long-range dynamics and meaningful patterns such as seasonalities, and practical , in that it can provide accurate long-range forecasts. EcoWeb consistently outperforms existing methods in terms of both accuracy and execution speed.