Institute for Numerical Simulation
Rheinische Friedrich-Wilhelms-Universität Bonn

  author = {J. Garcke and T. Gerstner and M. Griebel},
  title = {Intraday Foreign Exchange Rate Forecasting using Sparse
  year = {2013},
  doi = {10.1007/978-3-642-31703-3_4},
  volume = 88,
  pages = {81--105},
  note = {also available as INS Preprint No. 1006},
  series = {Lecture Notes in Computational Science and Engineering},
  editor = {J. Garcke and M. Griebel},
  booktitle = {Sparse grids and applications},
  publisher = {Springer},
  abstract = {We present a machine learning approach using the sparse
		  grid combination technique for the forecasting of intraday
		  foreign exchange rates. The aim is to learn the impact of
		  trading rules used by technical analysts just from the
		  empirical behaviour of the market. To this end, the problem
		  of analyzing a time series of transaction tick data is
		  transformed by delay embedding into a D-dimensional
		  regression problem using derived measurements from several
		  different exchange rates. Then, a grid-based approach is
		  used to discretize the resulting high-dimensional feature
		  space. To cope with the curse of dimensionality we employ
		  sparse grids in the form of the combination technique.
		  Here, the problem is discretized and solved for a
		  collection of conventional grids. The sparse grid solution
		  is then obtained by linear combination of the solutions on
		  these grids. We give the results of this approach to FX
		  forecasting using real historical exchange data of the
		  Euro, the US dollar, the Japanese Yen, the Swiss Franc and
		  the British Pound from 2001 to 2005.},
  annote = {other},
  inspreprintnum = {1006},
  pdf = { 1}