Change search
ReferencesLink to record
Permanent link

Direct link
Financial time series analysis: Chaos and neurodynamics approach
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2010 (English)Independent thesis Basic level (degree of Bachelor)Student thesis
Abstract [en]

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.

Place, publisher, year, edition, pages
Borlänge, 2010. , 61 p.
Keyword [en]
Chaos, fractals, neural networks, cognitive process. time series, stock markets, finance, artificial intelligence, Hurst, Lyapunov, Takens Embedding Theorem, Monte Carlo simulation, predictive modeling
URN: urn:nbn:se:du-4810OAI: oai:dalea.du.se:4810DiVA: diva2:518883
Available from: 2010-06-10 Created: 2010-06-10 Last updated: 2012-04-24Bibliographically approved

Open Access in DiVA

fulltext(1701 kB)1435 downloads
File information
File name FULLTEXT01.pdfFile size 1701 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Computer Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 1435 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 1238 hits
ReferencesLink to record
Permanent link

Direct link