Temporal pattern recognition in noisy non-stationary time series based on quantization into symbolic streams : Lessons learned from financial volatility trading

In this paper we investigate the potential of the analysis of noisy non-stationary time series by quantizing it into streams of discrete symbols and applying finite-memory symbolic predictors. The main argument is that careful quantization can reduce the noise in the time series to make model estimation more amenable given limited numbers of samples that can be drawn due to the non-stationarity in the time series....

MoreLess

Year of publication:	2000-07-01
Authors:	Tiňo, Peter ; Schittenkopf, Christian ; Dorffner, Georg
Institutions:	Institut für Informationsverarbeitung und -wirtschaft <Wien>
Published in:	Wirtschaftsuniversität Wien - Forschungsbezogene elektronische Publikationen SFB Adaptive Information Systems and Modelling in Economics and Management Science / Report Series ; 46 (2000)
Subject:	Volatilität \| Markov-Modell \| Markov Models \| Prognose \| Öffentliche Verwaltung \| public administration

freely available

Full text |

More access options

Check Google Scholar

In German libraries (KVK)

I need help

More details

Extent:	491520 bytes 26 p. application/pdf
Type of publication:	Book / Working Paper
Language:	English
Classification:	Business administration. Other aspects ; Individual Reports, Studies ; No country specification
Source:	USB Cologne (business full texts)

Persistent link: https://www.econbiz.de/10005841656