on the use of backward difference formulae to improve the prediction of direction in market related data

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ID: 201381
2013
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Abstract
The use of a BDF method as a tool to correct the direction of predictions made using curve fitting techniques is investigated. Random data is generated in such a fashion that it has the same properties as the data we are modelling. The data is assumed to have “memory” such that certain information imbedded in the data will remain within a certain range of points. Data within this period where “memory” exists—say at time steps t1,t2,…,tn—is curve-fitted to produce a prediction at the next discrete time step, tn+1. In this manner a vector of predictions is generated and converted into a discrete ordinary differential representing the gradient of the data. The BDF method implemented with this lower order approximation is used as a means of improving upon the direction of the generated predictions. The use of the BDF method in this manner improves the prediction of the direction of the time series by approximately 30%.
Reference Key
momoniat2013mathematicalon Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;E. Momoniat;C. Harley;M. Berman
Journal journal of power sources
Year 2013
DOI
10.1155/2013/652653
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