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32
M.N.Vrahatis, Financial forecasting through unsupervised clustering and evolutionary trained neural networks
 in: Congress on Evolutionary Computation
, 2003
"... In this paper, we review our work on a time series forecasting methodology based on the combination of unsupervised clustering and artificial neural networks. To address noise and nonstationarity, a common approach is to combine a method for the partitioning of the input space into a number of subs ..."
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Cited by 15 (8 self)
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In this paper, we review our work on a time series forecasting methodology based on the combination of unsupervised clustering and artificial neural networks. To address noise and nonstationarity, a common approach is to combine a method for the partitioning of the input space into a number of subspaces with a local approximation scheme for each subspace. Unsupervised clustering algorithms have the desirable property of deciding on the number of partitions required to accurately segment the input space during the clustering process, thus relieving the user from making this ad hoc choice. Artificial neural networks, on the other hand, are powerful computational models that have proved their capabilities on numerous hard realworld problems. The time series that we consider are all daily spot foreign exchange rates of major currencies. The experimental results reported suggest that predictability varies across different regions of the input space, irrespective of clustering algorithm. In all cases, there are regions that are associated with a particularly high forecasting performance. Evaluating the performance of the proposed methodology with respect to its profit generating capability indicates that it compares favorably with that of two other established approaches. Moving from the task of onestepahead to multiplestepahead prediction, performance deteriorates rapidly.
Computational Intelligence Methods for Financial Time Series Modeling
, 2005
"... this paper, the combination of unsupervised clustering algorithms with feedforward neural networks in exchange rate time series forecasting is studied. Unsupervised clustering algorithms have the desirable property of deciding on the number of partitions required to accurately segment the input s ..."
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Cited by 7 (3 self)
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this paper, the combination of unsupervised clustering algorithms with feedforward neural networks in exchange rate time series forecasting is studied. Unsupervised clustering algorithms have the desirable property of deciding on the number of partitions required to accurately segment the input space during the clustering process, thus relieving the user from making this ad hoc choice. Combining this input space partitioning methodology with feedforward neural networks acting as local predictors for each identified cluster helps alleviate the problem of nonstationarity frequently encountered in reallife applications. An improvement in the onestepahead forecasting accuracy was achieved compared to a global feedforward neural network model for the time series of the exchange rate of the German Mark to the US Dollar
HURST EXPONENT AND FINANCIAL MARKET PREDICTABILITY
, 2007
"... The Hurst exponent (H) is a statistical measure used to classify time series. H=0.5 indicates a random series while H>0.5 indicates a trend reinforcing series. The larger the H value is, the stronger trend. In this paper we investigate the use of the Hurst exponent to classify series of financial ..."
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Cited by 6 (1 self)
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The Hurst exponent (H) is a statistical measure used to classify time series. H=0.5 indicates a random series while H>0.5 indicates a trend reinforcing series. The larger the H value is, the stronger trend. In this paper we investigate the use of the Hurst exponent to classify series of financial data representing different periods of time. Experiments with backpropagation Neural Networks show that series with large Hurst exponent can be predicted more accurately than those series with H value close to 0.50. Thus Hurst exponent provides a measure for predictability.
Data Mining for Prediction. Financial Series Case
, 2003
"... Hard problems force innovative approaches and attention to detail, their exploration often contributing beyond the area initially attempted. This thesis investigates the data mining process resulting in a predictor for numerical series. The series experimented with come from financial data – usuall ..."
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Cited by 4 (0 self)
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Hard problems force innovative approaches and attention to detail, their exploration often contributing beyond the area initially attempted. This thesis investigates the data mining process resulting in a predictor for numerical series. The series experimented with come from financial data – usually hard to forecast. One approach to prediction is to spot patterns in the past, when we already know what followed them, and to test on more recent data. If a pattern is followed by the same outcome frequently enough, we can gain confidence that it is a genuine relationship. Because this approach does not assume any special knowledge or form of the regularities, the method is quite general – applicable to other time series, not just financial. However, the generality puts strong demands on the pattern detection – as to notice regularities in any of the many possible forms. The thesis ’ quest for an automated patternspotting involves numerous data mining and optimization techniques: neural networks, decision trees, nearest neighbors, regression, genetic algorithms and other. Comparison of their performance on a stock exchange index data is one of the contributions. As no single technique performed sufficiently well, a number of predictors have been put together, forming a voting ensemble. The vote is diversified not only by different training data – as usually done – but also by a learning method and its parameters. An approach is also proposed how to speedup a predictor finetuning. The algorithm development goes still further: A prediction can only be as good as the training data, therefore the need for good data preprocessing. In particular, new multivariate discretization and attribute selection algorithms are presented. The thesis also includes overviews of prediction pitfalls and possible solutions, as well as of ensemblebuilding for series data with financial characteristics, such as noise and many attributes. The Ph.D. thesis consists of an extended background on financial prediction, 7 papers, and 2 appendices.
Time Series Forecasting Methodology for MultipleStepAhead Prediction
 The IASTED International Conference on Computational Intelligence (CI 2005
, 2004
"... and applies it to generate multiplestepahead predictions for the direction of change of the daily exchange rate of the Japanese Yen against the US Dollar. The proposed methodology draws from the disciplines of chaotic time series analysis, clustering, and artificial neural networks. In brief, cl ..."
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Cited by 3 (3 self)
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and applies it to generate multiplestepahead predictions for the direction of change of the daily exchange rate of the Japanese Yen against the US Dollar. The proposed methodology draws from the disciplines of chaotic time series analysis, clustering, and artificial neural networks. In brief, clustering is applied to identify neighborhoods in the reconstructed state space of the system; and subsequently neural networks are trained to model the dynamics of each neighborhood separately. The results obtained through this approach are promising.
On developing a financial prediction system: Pitfalls and possibilities
 Proceedings of DMLL2002 Workshop at ICML2002
"... A successful financial prediction system presents many challenges. Some are encountered over again, and though an individual solution might be systemspecific, general principles still apply. Using them as a guideline might save time, effort, boost results, as such promoting project’s success. This ..."
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A successful financial prediction system presents many challenges. Some are encountered over again, and though an individual solution might be systemspecific, general principles still apply. Using them as a guideline might save time, effort, boost results, as such promoting project’s success. This paper remarks on a prediction system development stemming from author’s experiences and published results. The presentation follows stages in a prediction system development: data preprocessing, prediction algorithm selection and boosting, system evaluation – with some commonly successful solutions highlighted. 1.
A Hybrid Attribute Selection Approach for Text Classification
 JOURNAL OF THE ASSOCIATION FOR INFORMATION SYSTEMS
, 2010
"... The application of text mining in organizations is growing. Text classification, an important type of text mining problem, is characterized by a large attribute space and entails an efficient and effective attribute selection procedure. There are two general attribute selection approaches: the filte ..."
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Cited by 1 (0 self)
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The application of text mining in organizations is growing. Text classification, an important type of text mining problem, is characterized by a large attribute space and entails an efficient and effective attribute selection procedure. There are two general attribute selection approaches: the filter approach and the wrapper approach. While the wrapper approach is potentially more effective in finding the best attribute subset, it is costprohibitive in most text classification applications. In this paper, we propose a hybrid attribute selection approach that is both efficient and effective for text classification problems. We apply the proposed approach to detect and prevent Internet abuse in the workplace, which is becoming a major problem in modern organizations. The empirical evaluations we conducted using a variety of classification algorithms, indexing schemes, and attribute selection methods demonstrate the utility of the proposed approach. We found that combining the filter and wrapper approaches not only boosts the accuracies of text classifiers but also brings down the computational costs significantly.
Machine Learning in FX Carry Basket Prediction
"... Machines (RVM) were used to predict daily returns for an FX carry basket. Market observable exogenous variables known to have a relationship with the basket along with lags of the basket’s return were used as inputs into these methods. Combinations of these networks were used in a committee and simp ..."
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Machines (RVM) were used to predict daily returns for an FX carry basket. Market observable exogenous variables known to have a relationship with the basket along with lags of the basket’s return were used as inputs into these methods. Combinations of these networks were used in a committee and simple trading rules based on this amalgamated output were used to predict when carry basket returns would be negative for a day and hence a trader should go short this longbiased asset. The effect of using the networks for regression to predict actual returns was compared to their use as classifiers to predict whether the following day’s return would be up or down. Assuming highly conservative estimates of trading costs, over the 10.5 year (2751 trading day) rolling out of sample period investigated, improvements of 120 % in MAR ratio, 110 % in Sortino and 80 % in Sharpe relative to the ‘Always In ’ benchmark were found. Furthermore, the extent of the maximum drawdown was reduced by 19 % and the longest drawdown period was 53% shorter.
Nam," Artificial neural network modeling in forecasting successful implementation of ERP systems", international journal of computational intelligence research, vol 2
, 2006
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ORIGINAL ARTICLE
"... A general process for the development of peptidebased immunoassays for monoclonal antibodies ..."
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A general process for the development of peptidebased immunoassays for monoclonal antibodies