Modelling and forecasting of financial time series of "tick data" by functional analysis and neural networks

A functional method for time series forecasting is presented. Based on the splitting of the past dynamics into clusters, local models are built to capture the possible evolution of the series given the last known values. A probabilistic model is used to combine the local predictions. The method can be applied to any time series forecasting problem, but is particularly suited to data showing nonlinear dependencies, cluster effects, and observed at irregularly and randomly spaced times as financial series of "tick data" do. The method is applied to the forecasting of financial time series of tick data of IBM asset.