Qualification: 
Ph.D
nr_sakthivel@cb.amrita.edu

Dr. Sakthivel N. R. currently serves as Assistant Professor at Department of Mechanical Engineering, School of Engineering, Coimbatore Campus. His areas of research include Machine Learning, Condition Monitoring and Finite Element Methods.

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2017

Journal Article

B. B. Nair, Kumar, P. K. Saravana, Dr. Sakthivel N.R., and Vipin, U., “Clustering stock price time series data to generate stock trading recommendations: An empirical study”, Expert Systems with Applications, vol. 70, pp. 20 - 36, 2017.[Abstract]


Abstract Predicting the stock market is considered to be a very difficult task due to its non-linear and dynamic nature. Our proposed system is designed in such a way that even a layman can use it. It reduces the burden on the user. The user's job is to give only the recent closing prices of a stock as input and the proposed Recommender system will instruct him when to buy and when to sell if it is profitable or not to buy share in case if it is not profitable to do trading. Using soft computing based techniques is considered to be more suitable for predicting trends in stock market where the data is chaotic and large in number. The soft computing based systems are capable of extracting relevant information from large sets of data by discovering hidden patterns in the data. Here regression trees are used for dimensionality reduction and clustering is done with the help of Self Organizing Maps (SOM). The proposed system is designed to assist stock market investors identify possible profit-making opportunities and also help in developing a better understanding on how to extract the relevant information from stock price data. More »»

2016

Journal Article

Dr. Sakthivel N.R., Saravanamurugan, S., Nair, B. B., Dr. Elangovan M., and Sugumaran, V., “Effect of Kernel Function in Support Vector Machine for the Fault Diagnosis of Pump”, Journal of Engineering Science and Technology, vol. 11, pp. 826–838, 2016.[Abstract]


Pumps are widely used in a variety of applications. Defects and breakdown of these pumps will result in significant economic loss. Therefore, these must be under continuous observation. In various applications, the role of pump is decisive and condition monitoring is crucial. A completely automated on-line pump condition monitoring system which can automatically inform the operator of any faults, promising reduction in maintenance cost with a greater productivity saving both time and money.This paper presents the application of support vector machine for classification using statistical features extracted from vibration signals under good and faulty conditions of a pump. Effectiveness of various kernel functions of C-SVC and -SVC models are compared. The study gives some empirical guidelines for selecting an appropriate kernel in a classification problem.

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2014

Journal Article

Dr. Sakthivel N.R., Dr. Binoy B. Nair, Dr. Elangovan M., Sugumaran, V., and Saravanamurugan, S., “Comparison of Dimensionality Reduction Techniques for the Fault Diagnosis of Mono Block Centrifugal Pump Using Vibration Signals”, Engineering Science and Technology, an International Journal, vol. 17, pp. 30 - 38, 2014.[Abstract]


Bearing fault, Impeller fault, seal fault and cavitation are the main causes of breakdown in a mono block centrifugal pump and hence, the detection and diagnosis of these mechanical faults in a mono block centrifugal pump is very crucial for its reliable operation. Based on a continuous acquisition of signals with a data acquisition system, it is possible to classify the faults. This is achieved by the extraction of features from the measured data and employing data mining approaches to explore the structural information hidden in the signals acquired. In the present study, statistical features derived from the vibration data are used as the features. In order to increase the robustness of the classifier and to reduce the data processing load, dimensionality reduction is necessary. In this paper dimensionality reduction is performed using traditional dimensionality reduction techniques and nonlinear dimensionality reduction techniques. The effectiveness of each dimensionality reduction technique is also verified using visual analysis. The reduced feature set is then classified using a decision tree. The results obtained are compared with those generated by classifiers such as Naïve Bayes, Bayes Net and kNN. The effort is to bring out the better dimensionality reduction technique–classifier combination.

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2014

Journal Article

V. Muralidharan, Sugumaran, V., Indira, V., and Dr. Sakthivel N.R., “Fault Diagnosis of Monoblock Centrifugal Pump Using Stationary Wavelet Features and Bayes Algorithm”, Asian Journal of Science and Applied Technology, vol. 17, pp. 152 - 157, 2014.[Abstract]


Abstract Monoblock centrifugal pumps are employed in variety of critical engineering applications. Continuous monitoring of such machine component becomes essential in order to reduce the unnecessary break downs. At the outset, vibration based approaches are widely used to carry out the condition monitoring tasks. Particularly fuzzy logic, support vector machine (SVM) and artificial neural networks were employed for continuous monitoring and fault diagnosis. In the present study, the application of \{SVM\} algorithm in the field of fault diagnosis and condition monitoring is discussed. The continuous wavelet transforms were calculated for different families and at different levels. The computed transformation coefficients form the feature set for the classification of good and faulty conditions of the components of centrifugal pump. The classification accuracies of different continuous wavelet families at different levels were calculated and compared to find the best wavelet for the fault diagnosis of the monoblock centrifugal pump. More »»

2012

Journal Article

Dr. Sakthivel N.R., Dr. Binoy B. Nair, and Sugumaran, V., “Soft computing approach to fault diagnosis of centrifugal pump”, Applied Soft Computing, vol. 12, pp. 1574 - 1581, 2012.[Abstract]


Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). \{GEP\} is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using \{GEP\} is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both \{GEP\} and \{SVM\} equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study. More »»

2012

Journal Article

Dr. Sakthivel N.R., Sugumaran, V., and Dr. Binoy B. Nair, “Automatic rule learning using roughset for fuzzy classifier in fault categorization of mono-block centrifugal pump”, Applied Soft Computing, vol. 12, pp. 196 - 203, 2012.[Abstract]


Rule learning based approach to fault detection and diagnosis is becoming very popular, mainly due to their high accuracy when compared to older statistical methods. Fault detection and diagnosis of various mechanical components of centrifugal pump is essential to increase the productivity and reduce the breakdowns. This paper presents the use of rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a centrifugal pump. A fuzzy inference system (FIS) is built using rough set rules and tested using test data. The effect of different types of membership functions on the \{FIS\} performance is also presented. Finally, the performance of this classifier is compared to that of a fuzzy-antminer classifier and to multi-layer perceptron (MLP) based classifiers.

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2012

Journal Article

Dr. Sakthivel N.R., Dr. Binoy B. Nair, Sugumaran, V., and Roy, R. S., “Application of standalone system and hybrid system for fault diagnosis of centrifugal pump using time domain signals and statistical features”, International Journal of Data Mining, Modelling and Management, vol. 4, pp. 74-104, 2012.[Abstract]


Centrifugal pumps are a crucial part of many industrial plants. Early detection of faults in pumps can increase their reliability, reduce energy consumption, service and maintenance costs, and increase their life-cycle and safety, thus providing a significant reduction in life-time costs. Vibration-based condition monitoring and analysis using machine learning approach is gaining momentum. Vibration monitoring can identify a number of potential pump problems such as bearing fault, impeller fault, seal fault, loose joints or fasteners, and cavitation issues. This paper compares the fault classification efficiency of standalone decision tree classifier, standalone rough set classifier with hybrid systems such as decision tree-fuzzy classifier and rough set-fuzzy classifier. The results obtained using standalone systems are compared with the performance of hybrid systems. It is observed that standalone systems outperform the hybrid systems. More »»

2011

Journal Article

Dr. Sakthivel N.R., Dr. Binoy B. Nair, Sugumaran, V., and Rai, R. S., “Decision support system using artificial immune recognition system for fault classification of centrifugal pump”, International Journal of Data Analysis Techniques and Strategies, vol. 3, pp. 66-84, 2011.[Abstract]


Centrifugal pumps are a crucial part of many industrial plants. Early detection of faults in pumps can increase their reliability, reduce energy consumption, service and maintenance costs, and increase their life-cycle and safety, thus resulting in a significant reduction in life-time costs. Vibration analysis is a very popular tool for condition monitoring of machinery like pumps, turbines and compressors. The proposed method is based on a novel immune inspired supervised learning algorithm which is known as artificial immune recognition system (AIRS). This paper compares the fault classification efficiency of AIRS with hybrid systems such as principle component analysis (PCA)-Naïve Bayes and PCA-Bayes Net. The robustness of the proposed method is examined using its classification accuracy and kappa statistics. It is observed that the AIRS-based system outperforms the other two methods considered in the present study. More »»

2011

Journal Article

Va Muralidharan, Sugumaran, Va, and Dr. Sakthivel N.R., “Wavelet decomposition and support vector machine for fault diagnosis of monoblock centrifugal pump”, International Journal of Data Analysis Techniques and Strategies, vol. 3, pp. 159-177, 2011.[Abstract]


Monoblock centrifugal pumps play a very critical role in a variety of applications and condition monitoring of the various mechanical components of centrifugal pump becomes essential which in turn increases the productivity and reduces the breakdowns. Vibration-based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly, artificial neural networks fuzzy logic was employed for continuous monitoring and fault diagnosis. This paper presents the use of support vector machine (SVM) algorithm for fault diagnosis through discrete wavelet features extracted from vibration signals of good and faulty conditions of the components of centrifugal pump. The classification accuracies were computed for different types of classifiers such as artificial neural network (ANN), support vector machine (SVM) and J48 decision tree algorithm. Copyright © 2011 Inderscience Enterprises Ltd. More »»

2011

Journal Article

Vab Indira, Vasanthakumari, Rc, Dr. Sakthivel N.R., and Sugumaran, Ve, “Determination of sample size using power analysis and optimum bin size of histogram features”, International Journal of Data Analysis Techniques and Strategies, vol. 3, pp. 21-41, 2011.[Abstract]


Vibration signals are used in fault diagnosis of rotary machines as a source of information. Lots of work have been reported on identification of faults in roller bearing by using many techniques. Of late, application of machine learning approach in fault diagnosis is gaining momentum. Machine learning approach consists of chain of activities like, data acquisition, feature extraction, feature selection and feature classification. While histogram features are used, there are still a few questions to be answered such as how many histogram bins are to be used to extract features and how many samples to be used to train the classifier. This paper provides a mathematical study to choose the bin size and the minimum sample size to train the classifier using power analysis with statistical stability. A typical bearing fault diagnosis problem is taken as a case for illustration and the results are compared with that of entropy based algorithm (J48) for determining minimum sample size and bin size. Copyright © 2011 Inderscience Enterprises Ltd. More »»

2011

Journal Article

Va Indira, Vasanthakumari, Rb, Dr. Sakthivel N.R., and Sugumaran, Vd, “A method for calculation of optimum data size and bin size of histogram features in fault diagnosis of mono-block centrifugal pump”, Expert Systems with Applications, vol. 38, pp. 7708-7717, 2011.[Abstract]


Mono-block centrifugal pump plays a key role in various applications. Any deviation in the functions of centrifugal pump would lead to a monetary loss. Thus, it becomes very essential to avoid the economic loss due to malfunctioning of centrifugal pump. It is clear that the fault diagnosis and condition monitoring of pumps are important issues that cannot be ignored. Over the past 25 years, much research has been focused on vibration based techniques. Machine learning approach is one of the most widely used techniques using vibration signals in fault diagnosis. There are set of connected activities involved in machine learning approach namely, data acquisition, feature extraction, feature selection, and feature classification. Training and testing the classifier are the two important activities in the process of feature classification. When the histogram features are used as the representative of the vibration signals, a proper guideline has not been proposed so far to choose number of bins and number of samples required to train the classifier. This paper illustrates a systematic method to choose the number of bins and the minimum number of samples required to train the classifier with statistical stability so as to get best classification accuracy. In this study, power analysis method was employed to find the minimum number of samples required and a decision tree algorithm namely J48 was used to validate the results of power analysis and to find the optimum number of bins. © 2010 Elsevier Ltd. All rights reserved. More »»

2011

Journal Article

Dr. Saimurugan M., Dr. K. I. Ramachandran, Sugumaran, V., and Dr. Sakthivel N.R., “Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine”, Expert Systems with Applications, vol. 38, pp. 3819-3826, 2011.[Abstract]


The shaft and bearing are the most critical components in rotating machinery. Majority of problems arise from faulty bearings in turn affect the shaft. The vibration signals are widely used to determine the condition of machine elements. The vibration signals are used to extract the features to identify the status of a machine. This paper presents the use of c-SVC and nu-SVC models of support vector machine (SVM) with four kernel functions for classification of faults using statistical features extracted from vibration signals under good and faulty conditions of rotational mechanical system. Decision tree algorithm was used to select the prominent features. These features were given as inputs for training and testing the c-SVC and nu-SVC model of SVM and their fault classification accuracies were compared. © 2010 Elsevier Ltd. All rights reserved.

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2011

Journal Article

Dr. Elangovan M., S Devasenapati, B., Dr. Sakthivel N.R., and Ramachandran, K. I., “Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm”, Expert Systems with Applications, vol. 38, no. 4, pp. 4450–4459, 2011.[Abstract]


Tool wear and tool life are the principle areas are focus in any machining activity. The production rate, surface finish of machined component and the machine condition are directly related to the tool condition. This work on tool condition monitoring delves into data mining approach to discover the hidden information available in the tool vibration signals. The use of statistical features derived from the vibration data is used as the primary feature and Principle Component Analysis (PCA) transformed statistical features are evaluated as an alternative. In order to increase the robustness of the classifier and to reduce the data processing load, feature reduction is necessary. The feature reduction using (a) decision tree and (b) feature transformation and reduction using PCA are evaluated independently and the results are compared. The effective combination of feature reducer and classifier for designing the expert system is studied and reported. More »»

2011

Journal Article

Dr. Binoy B. Nair, Mohandas, V. P., and Dr. Sakthivel N.R., “Predicting stock market trends using hybrid ant-colony-based data mining algorithms: an empirical validation on the Bombay Stock Exchange”, International Journal of Business Intelligence and Data Mining, vol. 6, pp. 362-381, 2011.[Abstract]


Ant Colony Optimisation (ACO) algorithms use simple mutually cooperating agents (ants) to produce a robust and adaptive search system, which can be used for knowledge discovery. In this paper, a Support Vector Machine (SVM)-cAnt-Miner-based system for predicting the next-day’s trend in stock markets is proposed. The trend predicted by the proposed system is then used to identify the appropriate time to buy and sell securities. Performance of the proposed system is evaluated against SVM-Ant-Miner, SVM-Ant-Miner2, Naïve-Bayes and an Artificial Neural Network (ANN)-based trend prediction system. The results indicate that the proposed system outperforms all the other techniques considered. More »»

2011

Journal Article

Dr. Sakthivel N.R., Indira, V., Dr. Binoy B. Nair, and Sugumaran, V., “Use of histogram features for decision tree-based fault diagnosis of monoblock centrifugal pump”, International Journal of Granular Computing, Rough Sets and Intelligent Systems, vol. 2, pp. 23-36, 2011.[Abstract]


Monoblock centrifugal pumps are a crucial part of many industrial plants. Early detection of faults in pumps can increase their reliability, reduce energy consumption, service and maintenance costs, and increase their life-cycle and safety, thus resulting in a significant reduction in life-time costs. It is clear that the fault diagnosis and condition monitoring of pumps are important issues that cannot be ignored. Machine learning-based approach to fault detection and diagnosis is becoming very popular, mainly due to their high accuracy when compared to older statistical methods. There are set of related activities involved in machine learning approach namely, data acquisition from the monoblock centrifugal pump, feature extraction from the acquired data, feature selection, and finally feature classification. This paper presents the use of C4.5 decision tree algorithm for fault diagnosis through histogram feature extracted from vibration signals of good and faulty conditions of monoblock centrifugal pump. The performance of the proposed system is compared to that of a Naïve Bayes-based system to validate the superiority of the proposed system. More »»

2010

Journal Article

Dr. Sakthivel N.R., Sugumaran, V., and Dr. Binoy B. Nair, “Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump”, Mechanical Systems and Signal Processing, vol. 24, pp. 1887 - 1906, 2010.[Abstract]


Mono-block centrifugal pumps are widely used in a variety of applications. In many applications the role of mono-block centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approach is gaining momentum. Particularly, artificial neural networks, fuzzy logic have been employed for continuous monitoring and fault diagnosis. This paper presents the use of decision tree and rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a mono-block centrifugal pump. A fuzzy classifier is built using decision tree and rough set rules and tested using test data. The results obtained using decision tree rules and those obtained using rough set rules are compared. Finally, the accuracy of a principle component analysis based decision tree-fuzzy system is also evaluated. The study reveals that overall classification accuracy obtained by the decision tree-fuzzy hybrid system is to some extent better than the rough set-fuzzy hybrid system. More »»

2010

Journal Article

Dr. Sakthivel N.R., Sugumaran, Vb, and Babudevasenapati, Sa, “Vibration based fault diagnosis of monoblock centrifugal pump using decision tree”, Expert Systems with Applications, vol. 37, pp. 4040-4049, 2010.[Abstract]


Monoblock centrifugal pumps are widely used in a variety of applications. In many applications the role of monoblock centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly artificial neural networks, fuzzy logic were employed for continuous monitoring and fault diagnosis. This paper presents the use of C4.5 decision tree algorithm for fault diagnosis through statistical feature extracted from vibration signals of good and faulty conditions. More »»

2010

Journal Article

Dr. Binoy B. Nair, Mohandas, V. P., and Dr. Sakthivel N.R., “A decision tree—rough set hybrid system for stock market trend prediction”, International Journal of Computer Applications, vol. 6, pp. 1–6, 2010.[Abstract]


Prediction of stock market trends has been an area of great interest both to those who wish to profit by trading stocks in the stock market and for researchers attempting to uncover the information hidden in the stock market data. Applications of data mining techniques for stock market prediction, is an area of research which has been receiving a lot of attention recently. This work presents the design and performance evaluation of a hybrid decision tree- rough set based system for predicting the next days‟ trend in the Bombay Stock Exchange (BSESENSEX). Technical indicators are used in the present study to extract features from the historical SENSEX data. C4.5 decision tree is then used to select the relevant features and a rough set based system is then used to induce rules from the extracted features. Performance of the hybrid rough set based system is compared to that of an artificial neural network based trend prediction system and a naive bayes based trend predictor. It is observed from the results that the proposed system outperforms both the neural network based system and the naive bayes based trend prediction system. More »»

2010

Journal Article

Dr. Binoy B. Nair, Mohandas, V. P., and Dr. Sakthivel N.R., “A genetic algorithm optimized decision tree-SVM based stock market trend prediction system”, International Journal on Computer Science and Engineering, vol. 2, pp. 2981–2988, 2010.[Abstract]


Prediction of stock market trends has been an area of great interest both to researchers attempting to uncover the information hidden in the stock market data and for those who wish to profit by trading stocks. The extremely nonlinear nature of the stock market data makes it very difficult to design a system that can predict the future direction of the stock market with sufficient accuracy. This work presents a data mining based stock market trend prediction system, which produces highly accurate stock market forecasts. The proposed system is a genetic algorithm optimized decision tree-support vector machine (SVM) hybrid, which can predict one-day-ahead trends in stock markets. The uniqueness of the proposed system lies in the use of the hybrid system which can adapt itself to the changing market conditions and in the fact that while most of the attempts at stock market trend prediction have approached it as a regression problem, present study converts the trend prediction task into a classification problem, thus improving the prediction accuracy significantly. Performance of the proposed hybrid system is validated on the historical time series data from the Bombay stock exchange sensitive index (BSE-Sensex). The system performance is then compared to that of an artificial neural network (ANN) based system and a naïve Bayes based system. It is found that the trend prediction accuracy is highest for the hybrid system and the genetic algorithm optimized decision treeSVM hybrid system outperforms both the artificial neural network and the naïve bayes based trend prediction systems. More »»

2010

Journal Article

Dr. Sakthivel N.R., Sugumaran, V., and Dr. Binoy B. Nair, “Application of Support Vector Machine (SVM) and Proximal Support Vector Machine (PSVM) for fault classification of monoblock centrifugal pump”, International Journal of Data Analysis Techniques and Strategies, vol. 2, pp. 38-61, 2010.[Abstract]


Monoblock centrifugal pumps are widely used in a variety of applications. Defects and malfunctions (faults) of these pumps result in significant economic loss. Therefore, the pumps must be under constant monitoring. When a possible fault is detected, diagnosis is carried out to pinpoint it. In many applications, the role of monoblock centrifugal pumps is critical and condition monitoring is essential. Vibration-based condition monitoring and analysis using the machine-learning approach is gaining momentum. In particular, Artificial Neural Networks (ANNs), fuzzy logic and roughsets have been employed for condition monitoring and fault diagnosis. While it is difficult to train the neural network-based fault classifier, the classification accuracy in case of fuzzy logic- and roughest-based fault classifiers is not very high. This paper presents the use of Support Vector Machines (SVMs) and Proximal Support Vector Machines (PSVMs) for classifying faults using statistical features extracted from vibration signals under good and faulty conditions of a monoblock centrifugal pump. The Decision Tree (DT) algorithm is used to select prime features. These features are fed as inputs for training and testing SVMs and PSVMs and their fault classification accuracy is compared. The results are found to be better than neural network-, fuzzy- and roughest-based methods. More »»

Publication Type: Conference Paper

Year of Publication Publication Type Title

2015

Conference Paper

Dr. Elangovan M., Dr. Sakthivel N.R., Saravanamurugan, S., Dr. Binoy B. Nair, and Sugumaran, V., “Machine Learning Approach to the Prediction of Surface Roughness Using Statistical Features of Vibration Signal Acquired in Turning”, in Procedia Computer Science, 2015, vol. 50, pp. 282–288.[Abstract]


Abstract Prediction of surface roughness is always considered important in the manufacturing field. A product may require a particular roughness that may be specified by the designer for various reasons, either functional requirement or aesthetic appeal. While modern manufacturing systems and machines have always contributed towards better control of surface quality, better computational facilities and the availability of newer algorithms attract researchers to understand the prediction of quality in a better manner. In this paper, prediction of surface roughness by multiple regression analysis is presented. The predictors are cutting parameters, tool wear and the statistical parameters extracted from the vibration signals of a turning centre. The contribution of various statistical parameters in prediction of surface roughness is studied. A Machine learning approach using feature reduction using principle component analysis is attempted to achieve higher predictability and low computational effort.

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2010

Conference Paper

Dr. Binoy B. Nair, Mohandas, V. P., Dr. Sakthivel N.R., Nagendran, S., Nareash, A., Nishanth, R., Ramkumar, S., and Kumar, M. D., “Application of hybrid adaptive filters for stock market prediction”, in Proceedings of 2010 International Conference on Communication and Computational Intelligence, INCOCCI-2010, Perundurai, Erode, 2010, pp. 443-447.[Abstract]


Prediction of stock market trends has been an area of great interest both to those who wish to profit by trading stocks in the stock market and for researchers attempting to uncover the information hidden in the stock market data. Traditional techniques such technical analysis and signal processing techniques such as moving averages and regression have had limited success in predicting markets, which could be attributed to the dynamic behavior of the markets. In signal processing, adaptive filters have been widely used for efficient filtering of signals. However, the utilization of adaptive filters for prediction, especially of financial signals, has not received much attention in literature. In this study, hybrid adaptive filters are introduced for prediction to obtain highly accurate results. The hybrid filters used are DCT-LMS, DCT-NLMS, DCT-RLS and Kalman filters. The proposed method is used to predict the values of five of the largest stock markets, namely, BSE100, NASDAQ, NIKKEI225, S&P NIFTY, and FTSE100. The performance of hybrid adaptive filters is compared against the conventional filters like autoregressive (AR), Moving Average (MA) filters and adaptive filters like LMS, NLMS etc. The base technique considered is the Random Walk (RW) process which acts as the benchmark technique. The results show a high degree of prediction accuracy for the hybrid adaptive filters, which is very high when compared to conventional filters, thus indicating that hybrid adaptive filters can be successfully used for stock market prediction. © 2010 Kongu Engineering College. More »»

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