Article2001

A data mining approach to financial time series modelling and forecasting

Zoran Vojinović · Vojislav Kecman · Rainer Seidel

45 citations0/0 files storedIntelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Neural Networks and Applications Forecasting Techniques and Applications

Abstract

Abstract This paper describes one of the relatively new data mining techniques that can be used to forecast the foreign exchange time series process. The research aims to contribute to the development and application of such techniques by exposing them to difficult real‐world (non‐toy) data sets. The results reveal that the prediction of a Radial Basis Function Neural Network model for forecasting the daily $US/$NZ closing exchange rates is significantly better than the prediction of a traditional linear autoregressive model in both directional change and prediction of the exchange rate itself. We have also investigated the impact of the number of model inputs (model order), the number of hidden layer neurons and the size of training data set on prediction accuracy. In addition, we have explored how the three different methods for placement of Gaussian radial basis functions affect its predictive quality and singled out the best one. Copyright © 2001 John Wiley & Sons, Ltd.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 45 citations exist upstream.

References

1977Time Series Analysis: Forecasting and Control 1994Time series prediction: Forecasting the future and understanding the past 1994Bankruptcy prediction using neural networks 1996Neural Network Models for Time Series Forecasts 1991Time series forecasting using neural networks vs. Box- Jenkins methodology 1993Feedforward Neural Nets as Models for Time Series Forecasting 1997A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks 1996Hybrid neural network models for bankruptcy predictions

Cited by

2015Regularized dynamic self-organized neural network inspired by the immune algorithm for financial time series prediction

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.