Non-linear financial time series forecasting -
Application to the Bel 20 stock market index

Lendasse, Amaury; Bodt, Eric de; Wertz, Vincent; Verleysen, Michel

doi:10.1051/ejess:2000110

Cited by 109 publications

(46 citation statements)

References 19 publications

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“…4. BEL20 Market Index from December 1st, 1987to February 14th, 1998 y t Lendasse et al (2000). Nevertheless, it must be mentioned that problems related to a difficult convergence of nonlinear models add to the difficulty of choosing the parameters in our methodology, making the results more difficult to illustrate.…”

Section: Discussionmentioning

confidence: 99%

Dimension reduction of technical indicators for the prediction of financial time series - Application to the BEL20 Market Index

Lendasse¹,

Lee²,

Bodt

et al. 2001

E.J.E.S.S.

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Abstract. -Prediction of financial time series using artificial neural networks has been the subject of many publications, even if the predictability of financial series remains a subject of scientific debate in the financial literature. Facing this difficulty, analysts often consider a large number of exogenous indicators, which makes the fitting of neural networks extremely difficult. In this paper, we analyze how to aggregate a large number of indicators in a smaller number using -possibly nonlinear-projection methods. Nonlinear projection methods are shown to be equivalent to the linear Principal Component Analysis when the prediction tool used on the new variables is linear. Furthermore, the computation of the nonlinear projection gives an objective way to evaluate the number of resulting indicators needed for the prediction. Finally, the advantages of nonlinear projection could be further exploited by using a subsequent nonlinear prediction model. The methodology developed in the paper is validated on data from the BEL20 market index, using systematic cross-validation results.

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Section: Discussionmentioning

confidence: 99%

Dimension reduction of technical indicators for the prediction of financial time series - Application to the BEL20 Market Index

Lendasse¹,

Lee²,

Bodt

et al. 2001

E.J.E.S.S.

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“…The over-fitting problem results from model complexity. Handling this problem becomes more difficult when there are many input variables [13]. Therefore, choosing a set of most relevant and non-redundant input variables is necessary to build an appropriate model with high performance, and to improve the interpretability of the selected set of inputs [14].…”

Section: Input Variables Selection Methodologymentioning

confidence: 99%

Input Variables Selection Using Mutual Information for Neuro Fuzzy Modeling with the Application to Time Series Forecasting

Yousefi

Mirmomeni

Lucas

2007

2007 International Joint Conference on Neural Networks

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Abstract-This paper presents a methodology to select input variables for time series prediction. A main motivation is to find some proper input variables which describe the time series dynamics properly. It is shown that even when the choice of input variables is confined to the lagged values of the process to be predicted, a nonlinear analysis of the most significant factors is crucial for improving the prediction quality. The proposed method is used to select the appropriate input variables for neuro fuzzy models utilized for time series prediction benchmark in NN3 competition as well as a second benchmark to show the generality of the claims. Results depict the effectiveness of the proposed method in proper input selection for neuro fuzzy models for prediction task.

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“…Obviously, the quantity of information increases with the number of variables and may cause different type of problems (as can be seen in next subsection). In these environments, a feasible methodology [23,32,47] is to choose the largest possible number of variables to be taken into account (past values of the series and exogenous variables that could influence the series) and then apply feature selection methods in order to transform the initial set of variables into another smaller set of state variables, keeping as much of the information contained in the original set as possible.…”

Section: Time Series Analysis and Forecastingmentioning

confidence: 99%

“…Different examples of RBFNs applied to financial analysis can be found in [47,50,59,77,81], for forecast stock market index, exchange-trade fund DIA, stock data and financial time series, respectively with the following main characteristics:…”

Section: Rbfn Design Processesmentioning

confidence: 99%

A study on the medium-term forecasting using exogenous variable selection of the extra-virgin olive oil with soft computing methods

et al. 2011

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Time series forecasting is an important task for the business sector. Agents involved in the olive oil sector consider that, for the olive oil price, medium-term predictions are more important than short-term predictions. In collaboration with these agents the forecasting of the price of extra-virgin olive oil six months ahead has been established as the aim of this work. According to expert opinion, the use of exogenous variables and technical indicators can help in this task and must be included in the forecasting process. The amount of variables that can be considered makes necessary the use of feature selection algorithms in order to reduce the number of variables and to increase the interpretability and usefulness of the obtained forecasting system. Thus, in this paper CO 2 RBFN, a cooperativecompetitive algorithm for Radial Basis Function Network design, and other soft computing methods have been applied to the data sets with the whole set of input variables and to the data sets with the selected set of input variables. The experimentation carried out shows that CO 2 RBFN obtains the best results in medium term forecasting for olive oil prices with the whole and with the selected set of input variables. Moreover, the feature selection methods applied to the data sets highlighted some influential variables which could be considered not only for the prediction but also for the description of the complex process involved in the mediumterm forecasting of the olive oil price.

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Non-linear financial time series forecasting - Application to the Bel 20 stock market index

Cited by 109 publications

References 19 publications

Dimension reduction of technical indicators for the prediction of financial time series - Application to the BEL20 Market Index

Dimension reduction of technical indicators for the prediction of financial time series - Application to the BEL20 Market Index

Input Variables Selection Using Mutual Information for Neuro Fuzzy Modeling with the Application to Time Series Forecasting

A study on the medium-term forecasting using exogenous variable selection of the extra-virgin olive oil with soft computing methods

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