Ranjeeta Bisoi

A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter

Abstract Stock market prediction is of great interest to stock traders and investors due to high profit in trading the stocks. A successful stock buying/selling generally occurs near price trend turning point. Thus the prediction of stock market indices and its analysis are important to ascertain whether the next days closing price would increase or decrease. This paper, therefore, presents a simple IIR filter based dynamic neural network (DNN) and an innovative optimized adaptive unscented Kalman filter for forecasting stock price indices of four different Indian stocks, namely the Bombay stock exchange (BSE), the IBM stock market, RIL stock market, and Oracle stock market. The weights of the dynamic neural information system are adjusted by four different learning strategies that include gradient calculation, unscented Kalman filter (UKF), differential evolution (DE), and a hybrid technique (DEUKF) by alternately executing the DE and UKF for a few generations. To improve the performance of both the UKF and DE algorithms, adaptation of certain parameters in both these algorithms has been presented in this paper. After predicting the stock price indices one day to one week ahead time horizon, the stock market trend has been analyzed using several important technical indicators like the moving average (MA), stochastic oscillators like K and D parameters, WMS%R (William indicator), etc. Extensive computer simulations are carried out with the four learning strategies for prediction of stock indices and the up or down trends of the indices. From the results it is observed that significant accuracy is achieved using the hybrid DEUKF algorithm in comparison to others that include only DE, UKF, and gradient descent technique in chronological order. Comparisons with some of the widely used neural networks (NNs) are also presented in the paper.

A self adaptive differential harmony search based optimized extreme learning machine for financial time series prediction

Abstract This paper proposes a hybrid learning framework called Self Adaptive Differential Harmony Search Based Optimized Extreme Learning Machine (SADHS-OELM) for single hidden layer feed forward neural network (SLFN). The new learning paradigm seeks to take advantage of the generalization ability of extreme learning machines (ELM) along with the global learning capability of a self adaptive differential harmony search technique in order to optimize the fitting performance of SLFNs. SADHS is a variant of harmony search technique that uses the current to best mutation scheme of DE in the pitch adjustment operation for harmony improvisation process. SADHS has been used for optimal selection of the hidden layer parameters, the bias of neurons of the hidden-layer, and the regularization factor of robust least squares, whereas ELM has been applied to obtain the output weights analytically using a robust least squares solution. The proposed learning algorithm is applied on two SLFNs i.e. RBF and a low complexity Functional link Artificial Neural Networks (CEFLANN) for prediction of closing price and volatility of five different stock indices. The proposed learning scheme is also compared with other learning schemes like ELM, DE-OELM, DE, SADHS and two other variants of harmony search algorithm. Performance comparison of CEFLANN and RBF with different learning schemes clearly reveals that CEFLANN model trained with SADHS-OELM outperforms other learning methods and also the RBF model for both stock index and volatility prediction.

Classification of power quality data using decision tree and chemotactic differential evolution based fuzzy clustering

Abstract This paper presents a new approach for processing various non-stationary power quality waveforms through a Fast S-Transform with modified Gaussian window to generate time–frequency contours for extracting relevant feature vectors for automatic disturbance pattern classification. The extracted features are then clustered using Bacterial Foraging Optimization Algorithm (BFOA) based Fuzzy decision tree to give improved classification accuracy in comparison to the Fuzzy decision tree alone. To circumvent the problem of premature convergence of BFOA and to improve classification accuracy further, a hybridization of BFOA (Bacterial Foraging Optimization Algorithm) with another very popular optimization technique of current interest called Differential Evolution (DE) is presented in this paper. For robustness the mutation loop of the DE algorithm has been made variable in a stochastic fashion. This hybrid algorithm (Chemotactic Differential Evolution Algorithm (CDEA)) is shown to overcome the problems of slow and premature convergence of BFOA and provide significant improvement in power signal pattern classification.

A differential harmony search based hybrid interval type2 fuzzy EGARCH model for stock market volatility prediction

In this paper a new hybrid model integrating an interval type2 fuzzy logic system (IT2FLS) with a computationally efficient functional link artificial neural network (CEFLANN) and an Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model has been proposed for accurate forecasting and modeling of financial data with changing variance over time. The proposed model denoted as IT2F-CE-EGARCH helps to enhance the ability of EGARCH model through a joint estimation of the important features of EGARCH like leverage effect, asymmetric shock by leverage effect with the secondary membership functions of interval type2 TSK FLS and the functional expansion and learning component of a CEFLANN. The secondary membership functions with upper and lower limits of IT2FLS provide a forecasting interval for handling more complicated uncertainties involved in volatility forecasting compared to type1 FLS. The performance of the proposed model has been observed with two membership functions i.e. Gaussian with fixed mean, uncertain variance and Gaussian with fixed variance and uncertain mean. The proposed model has also been compared with a few other fuzzy time series models and GARCH family models based on four performance metrics: MSFE, RMSFE, MAFE and Rel MAE. Again a differential harmony search (DHS) algorithm has been suggested for optimizing the parameters of all the fuzzy time series models. The results indicate that the proposed IT2F-CE-EGARCH model offers significant improvements in volatility forecasting performance in comparison with all other specified models over BSE Sensex and CNX Nifty dataset. A hybrid interval type2 Fuzzy-FLANN EGARCH model is proposed to forecast the volatility of three stock market indexes.The TSK-type interval type2 fuzzy inference system uses FLANN in the consequent part of the fuzzy rules for improved mapping.The leverage effect, asymmetric shock by leverage effect of EGARCH model are important for forecasting.A harmony search (HS) based learning strategy is used for EGARCH-FLANN model parameters.Statistical tests are also included for choosing the right model.

A hybrid functional link dynamic neural network and evolutionary unscented Kalman filter for short-term electricity price forecasting

Electricity price is an important aspect in a restructured power market. Because of nonlinearity, nonstationary, and volatility of electricity price, it is highly essential to forecast the price on a short-term scale. This paper presents an efficient method based on a hybrid functional link dynamic neural network (DNN) trained by an adaptive robust unscented Kalman filter (UKF). The proposed method forecasts hourly prices for the day-ahead electricity market. The functional block helps to introduce nonlinearity by expanding the input space to higher-dimensional space through a basis function without using any hidden layer like multilayer perceptron structure. The DNN includes one or more infinite impulse response filters in the forward path providing feedback connections between outputs and inputs. This allows signal flow in both forward and backward directions, giving the network a dynamic memory useful to mimic dynamic systems. Also to improve the accuracy of the forecast, the noise covariance matrices of the UKF are adapted recursively. The proposed method is tested on PJM electricity market, and the residuals mean absolute error is compared with other forecasting methods, indicating the improved accuracy of the approach and its suitability to produce a real-time forecast. Further, to compare the accuracy of the forecast, an alternative UKF noise covariance optimization is attempted using differential evolution.

Data decomposition based fast reduced kernel extreme learning machine for currency exchange rate forecasting and trend analysis

Abstract In this paper, we propose a hybrid forecasting model that combines Empirical Mode Decomposition (EMD) with fast reduced kernel Extreme Learning Machine (KELM) for day ahead foreign currency exchange rate forecasting. EMD is an efficient method for nonlinear data decomposition in such a noisy environment and the purpose is to find important components in terms of Intrinsic Mode Functions (IMFs) by which the nonlinear time series is converted into stationary time series by making the data smoother and simpler for analysis. The average IMFs decomposed from EMD (AEMD) are hybridized with fast KELM named as AEMD-KELM for producing a more accurate forecast. The experimental results using AEMD-KELM method for seven currency exchange rates like CAD/HKD, CAD/CNY, CAD/USD, CAD/BRL, CAD/JPY, EUR/USD, and GBP/USD provide superior prediction and trend analysis in comparison with EMD based ELM (EMD-ELM) approaches. Further currency exchange rate movement trends are used for generating trading signals like buy, sell or hold.

Comparison of modified teaching---learning-based optimization and extreme learning machine for classification of multiple power signal disturbances

This paper presents a modified TLBO (teaching–learning-based optimization) approach for the local linear radial basis function neural network (LLRBFNN) model to classify multiple power signal disturbances. Cumulative sum average filter has been designed for localization and feature extraction of multiple power signal disturbances. The extracted features are fed as inputs to the modified TLBO-based LLRBFNN for classification. The performance of the proposed modified TLBO-based LLRBFNN model is compared with the conventional model in terms of convergence speed and classification accuracy. Also, an extreme learning machine (ELM) approach is used to optimize the performance of the proposed LLRBFNN and is compared with the TLBO method in classifying the multiple power signal disturbances. The classification results reveal that although the TLBO approach produces slightly better accuracy in comparison with the ELM approach, the latter is much faster in implementation, thus making it suitable for processing large quantum of power signal disturbance data.

Image denoising using neural network based Accelerated Particle Swarm Optimization

This paper proposes a new type of filter called neural network filter for image denoising. The noisy images are fed as input to the network and the weights are updated with LMS algorithm. Further the weights are updated by using a recently developed novel optimization technique called Accelerated Particle Swarm Optimization (APSO) for gray level image denoising application. Implementation of APSO is easier than the PSO(Particle Swarm Optimization) and GA(Genetic Algorithm) in terms of computational time. The results of comparison of the optimization has been presented.

Prediction of financial time series and its volatility using a hybrid dynamic neural network trained by sliding mode algorithm and differential evolution

A dynamic neural network (DNN) and a new computationally efficient functional link artificial neural network (CEFLANN) combination optimised with differential evolution (DE) is presented in this paper to predict financial time series like stock price indices and stock return volatilities of two important Indian stock markets, namely the Reliance Industries Limited (RIL), and NIFTY from one day ahead to one month in advance. The DNN comprises a set of 1st order IIR filters for processing the past inputs and their functional expansions and its weights are adjusted using a sliding mode strategy known for its fast convergence and robustness with respect to chaotic variations in the inputs. Extensive computer simulations are carried out to predict simultaneously the stock market indices and return volatilities and it is observed that the simple IIR-based DNN-FLANN model hybridised with DE produces better forecasting accuracies in comparison to the more complicated neural architectures.

Financial time series prediction using a hybrid functional link fuzzy neural network trained by adaptive unscented kalman filter

The model proposed in this paper, is a hybridization of fuzzy neural network (FNN) and a functional link neural system for currency exchange rate prediction. The TSK-type feedforward fuzzy neural network does not take the full advantage of the use of the fuzzy rule base in accurate input-output mapping and hence a hybrid model is developed using the functional link neural network (FLANN) to construct the consequent part of the fuzzy rules. The FLANN model is used to provide an expanded nonlinear transformation to the input space thereby increasing its dimension which will be adequate to capture the nonlinearities and chaotic variations in the currency exchange time series. Further hybridizing it with Fuzzy neural network will result in a significant accuracy in day ahead currency exchange rate prediction. Currency exchange rates between US Dollar (USD) and other four currencies such as Australian Dollar (AUD), Indian Rupee (INR), Japanese Yen (JPY), and Canadian Dollar (CAD) datasets are used to validate the efficacy of the proposed FLFNN.