Simona M l roiu, Kimmo Kiviluoto and Erkki Oja, Helsinki University of Technology,
Simona.Malaroiu@hut.fi, Kimmo.Kiviluoto@hut.fi, Erkki.Oja@hut.fi

We propose a new method to predict time series us- ing the technique of Independent Component Analysis (ICA) as a preprocessing tool. If certain assumptions hold, we show that ICA can be used to transform a set of time series into another set that is easier to predict. These assumptions are not unrealistic for many real- world time series, including nancial time series. After prediction, the original time series can be constructed by the inverse ICA transform. We have tested this approach on two sets of data: articial toy data and nancial time series. The results suggest that these can be predicted more accurately using the ICA pre- processing.