Boosting a weak learning algorithm by majority
Information and Computation
Non-linear noise reduction and detecting chaos: some evidence from the S&P composite price index
Mathematics and Computers in Simulation - Special issue from IMACS sponsored conference: “MODSIM 97”
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Model Selection Procedures for Predicting Turning Points in Financial Time Series
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Identifying Temporal Patterns for Characterization and Prediction of Financial Time Series Events
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Neural Computing and Applications
Expert Systems with Applications: An International Journal
Dynamic adaptive ensemble case-based reasoning: application to stock market prediction
Expert Systems with Applications: An International Journal
Application of bayesian techniques for MLPs to financial time series forecasting
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
The prediction of the financial time series based on correlation dimension
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Strategic group identification using evolutionary computation
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Turning points prediction has long been a tough task in the field of time series analysis due to its strong nonlinearity, and thus has attracted many research efforts. In this study, the turning points prediction (TPP) framework is presented and further employed to develop a novel trading strategy designing approach to financial investment. The TPP framework is a machine learning-based solution incorporating chaotic dynamic analysis and neural network modeling. It works on the ground of a nonlinear mapping deduced in financial time series through chaotic analysis. An event characterization method is created in TTP framework to characterize trend patterns in ongoing financial time series. The main contributions of this paper are (1) it presents an ensemble learning based TPP framework, within which the nonlinear mapping is approximated by the ensemble artificial neural network (EANN) model with a new parameters learning algorithm; (2) a genetic algorithm (GA) based threshold optimization procedure is described with a newly defined performance measure, named TpMSE, which is used as a cost function; and (3) a trading strategy designing approach is proposed based on the TPP framework. The proposed approach was applied to the two real-world financial time series, i.e., an individual stock quote time series and the Dow Jones Industrial Average (DJIA) index time series. Experimental results show that the proposed approach can help investors make profitable decisions.