Local Linear Wavelet Neural Network Research Articles

A hybrid approach for forecasting bitcoin series

Bitcoin price prediction is a substantial challenge for cryptocurrency investors. This study offers an innovative scheme to predict Bitcoin returns and volatilities using a hybrid model that incorporates the autoregressive fractionally integrated moving average (ARFIMA), empirical wavelet (EW) transform, and local linear wavelet neural network (LLWNN) approaches to produce an ARFIMA-EWLLWNN model. Our methodologies integrate the advantages of the long-memory model, EW decomposition technique, artificial neural network structure, and backpropagation and particle swarm optimization learning algorithms. The experimental results of the optimized hybrid approach outperform some classic models by providing more accurate out-of-sample forecasts over longer horizons. The model proves to be the most appropriate Bitcoin forecasting technique. Moreover, the implemented method produces smaller prediction errors than other computing techniques.

Coupling the Empirical Wavelet and the Neural Network Methods in Order to Forecast Electricity Price

This paper aims to evaluate the forecast capability of electricity markets, categorized by numerous major characteristics such as non-stationarity, nonlinearity, highest volatility, high frequency, mean reversion and multiple seasonality, which give multifarious forecasts. To improve it, this investigation proposes a new hybrid approach that links a dual long-memory process (Gegenbauer autoregressive moving average (GARMA) and generalized long-memory GARCH (G-GARCH)) and the empirical wavelet transform (EWT) and local linear wavelet neural network (LLWNN) approaches, forming the k-factor GARMA-EWLLWNN model. The future hybrid model accomplished is assessed via data from the Polish electricity markets, and it is matched with the generalized long-memory k-factor GARMA-G-GARCH process and the hybrid EWLLWNN, to demonstrate the robustness of our approach. The obtained outcomes show that the suggested model presents important results to define the relevance of the modeling approach that offers a remarkable framework to reproduce the inherent characteristics of the electricity prices. Finally, it is presented that the adopted methodology is the most appropriate one for prediction as it realizes a better prediction performance and may be an answer for forecasting electricity prices.

A novel optimization of hybrid feature selection algorithms for image classification technique using RBFNN and MFO

A brain tumor develops when abnormal cells in brain tissue multiply uncontrollably. For radiologists, finding and categorizing tumors manually has become a demanding and time-consuming task. When radiologists or other clinical professionals need to extract an infected tumor area from an MR picture, they have to go through a lengthy and laborious process. To improve performance and simplify the segmentation process, we investigate the FCM-predicted picture segmentation techniques in this study. In addition, classifiers for automating the detection and reclassification of encephalon tumors receive input consisting of critical information obtained from each segmented tissue. We have assessed, verified, and demonstrated the experimental efficacy of the proposed method. The purpose of this research was to develop a novel MFO (Moth-Flame Optimization) based LLRBFNN model for the automatic detection and classification of benign and malignant brain tumors. In order to alleviate the burden of manually detecting encephalon cancers from MR images, the suggested LLRBFNN model parameters are improved via MFO training. The Modified FCM method removes outlying nodes from the LLRBFNN model, and the MFO algorithm keeps the current of node centres in the aforementioned model. The proposed MFO-LLRBFNN model was evaluated alongside the Decision Tree and the Random Forest. To prove the reliability of this model, an MFO-based LLWNN (Local Linear Wavelet Neural Network) model for autonomously detecting brain cancers was presented. We extracted features from MR images using the MFCM (modified fuzzy C-Means) segmentation algorithm and the GLCM (Gray Level Co-occurrence Matrix) technique.

A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting

This paper proposes a hybrid modelling approach for forecasting returns and volatilities of the stock market. The model, called ARFIMA-WLLWNN model, integrates the advantages of the ARFIMA model, the wavelet decomposition technique (namely, the discrete MODWT with Daubechies least asymmetric wavelet filter) and artificial neural network (namely, the LLWNN neural network). The model develops through a two-phase approach. In phase one, a wavelet decomposition improves the forecasting accuracy of the LLWNN neural network, resulting in the Wavelet Local Linear Wavelet Neural Network (WLLWNN) model. The Back Propagation and Particle Swarm Optimization (PSO) learning algorithms optimize the WLLWNN structure. In phase two, the residuals of an ARFIMA model of the conditional mean become the input to the WLLWNN model. The hybrid ARFIMA-WLLWNN model is evaluated using daily returns of the Dow Jones Industrial Average index over 01/05/2010 to 02/11/2020. The experimental results indicate that the PSO-optimized version of the hybrid ARFIMA-WLLWNN outperforms the LLWNN, WLLWNN, ARFIMA-LLWNN, and the ARFIMA-HYAPARCH models and provides more accurate out-of-sample forecasts over validation horizons of one, five and twenty-two days.

Improving Time Series Prediction With Feature Selection Using a Velocity-Enhanced Whale Optimization Algorithm

The nonlinearity and uncertain behavior of many recent financial applications is increasing rapidly. Thus, it is important to resolve the rapid growth of time-variant problems with the help of artificial intelligence methods. In this paper, a hybridized method is used to predict four types of financial datasets: absenteeism at work, blog feedback data, currency exchange rate, and energy consumption. The prediction accuracy is improved with feature selection techniques. During the use of feature selection methods, only related features are carefully chosen and then fed to the neural network algorithm for prediction. In this research, the previous year data is taken for training and recent year data is taken for testing. Finally, the results of the velocity enhanced whale optimization algorithm (VEWOA) is compared with other methods like local linear wavelet neural network (LLWNN) and local linear radial basis functional neural network (LLRBFNN).

A New Hybrid Wavelet-Neural Network Approach for Forecasting Electricity

This study investigates the performance of a novel neural network technique in the problem of price forecasting. To improve the prediction accuracy using each model’s unique features, this research proposes a hybrid approach that combines the -factor GARMA process, empirical wavelet transform and the local linear wavelet neural network (LLWNN) methods, to form the GARMA-WLLWNN process. In order to verify the validity of the model and the algorithm, the performance of the proposed model is evaluated using data from Polish electricity markets, and it is compared with the dual generalized long memory -factor GARMA-G-GARCH model and the individual WLLWNN. The empirical results demonstrated the proposed hybrid model can achieve a better predicting performance and prove that is the most suitable electricity market forecasting technique.

Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting

This paper proposes a hybrid modelling approach for forecasting returns and volatilities of the stock market. The model, called ARFIMA-WLLWNN model, integrates the advantages of the ARFIMA model, the wavelet decomposition technique (namely, the discrete MODWT with Daubechies least asymmetric wavelet filter) and artificial neural network (namely, the LLWNN neural network). The model develops through a two-phase approach. In phase one, a wavelet decomposition improves the forecasting accuracy of the LLWNN neural network, resulting in the Wavelet Local Linear Wavelet Neural Network (WLLWNN) model. The Back Propagation (BP) and Particle Swarm Optimization (PSO) learning algorithms optimize the WLLWNN structure. In phase two, the residuals of an ARFIMA model of the conditional mean become the input to the WLLWNN model. The hybrid ARFIMA-WLLWNN model is evaluated using daily closing prices for the Dow Jones Industrial Average (DJIA) index over 01/01/2010 to 02/11/2020. The experimental results indicate that the PSO-optimized version of the hybrid ARFIMA-WLLWNN outperforms the LLWNN, WLLWNN, ARFIMA-LLWNN, and the ARFIMA-HYAPARCH models and provides more accurate out-of-sample forecasts over validation horizons of one, five and twenty-two days.

Elephant herding optimization technique based neural network for cancer prediction

Abstract Cancer is a kind of uncontrolled growth of abnormal cells in any part of the body. It can be of many types. Early prognosis of cancer is the only way to treat it in a better way for researchers. It is very important to classify cancer in high or low risk group and that can be done by applying different machine learning techniques. In this study, a nature-based machine learning technique is developed named as Elephant Herding Optimization algorithm (EHO) which is validated using some cancer datasets like lung cancer, breast cancer, and cervical cancer. Here, the feature selection algorithm like ANOVA and Kruskal-Wallis tests are used where the relevant number of features are selected. The performance of EHO is evaluated using Root Mean Square Error (RMSE) and Correct Classification Rate (CCR) with and without feature selection. The RMSE value of the EHO algorithm is compared with Local Linear Wavelet Neural Network (LLWNN) and Particle Swarm Optimization (PSO). EHO shows 0.9837 CCR in the breast cancer dataset, 0.9671 CCR in the cervical cancer dataset, and 0.8821 CCR in the lung cancer dataset using ANOVA test i.e. the best result in comparison to other optimization algorithms. According to the tables it is clear that classification techniques takes more time without feature selection techniques but less time with feature selection techniques.

Forecasting electricity spot price for Nord Pool market with a hybridk‐factor GARMA–LLWNN model

AbstractThis paper proposes a new hybrid approach, based on the combination of parametric and nonparametric models by adopting wavelet estimation approach, to model and predict the price electricity for Nord Pool market. Our hybrid methodology consists into two steps. The first step aims at modeling the conditional mean of the time series, using a generalized fractional model withk‐factor of Gegenbauer termed thek‐factor GARMA model; the parameters of this model are estimated using the wavelet approach based on the discrete wavelet packet transform (DWPT). The second step aims at estimating the conditional variance, so we adopt the local linear wavelet neural network (LLWNN) model. The proposed hybrid model is tested using the hourly log‐returns of electricity spot price from the Nord Pool market. The empirical results were compared with the predictions of the ARFIMA–LLWNN, thek‐factor GARMA–FIGARCH and the individual LLWNN models. It is shown that the proposed hybridk‐factor GARMA–LLWNN model outperforms all other competing methods. Hence it is a robust tool in forecasting time series.

Bidirectional reservoir networks trained using SVM $$+$$ + privileged information for manufacturing process modeling

In the last decade, a wide range of machine learning approaches were proposed and experimented to model highly nonlinear manufacturing processes. However, improving the performance of such models is challenging due to the complexity and high dimensionality of the manufacturing processes in general. In this paper, we propose bidirectional echo state reservoir networks (Bi-ESNs) trained using support vector machine privileged information method (SVM\(+\)) to model a winding machine process. The proposed model will be applied, tested and compared to reported models in the literature such as classical ESN with linear regression, ESN with a linear SVM readout, genetic programming, feedfoward neural network with backpropagation, radial basis function network, adaptive neural fuzzy inference system and local linear wavelet neural network. The developed results show that Bi-ESNs trained with SVM\(+\) are promising. It was able to provide better generalization performance compared to other models.

Dynamic electricity price forecasting using local linear wavelet neural network

Price forecasting has become one of the main focuses of electric power market research efforts as price is the key index to evaluate the market competition efficiency and reflects the operation condition of electricity market decision making. The work presented in this paper makes use of local linear wavelet neural networks to find the market clearing price for a given period, which is based on similar days approach. The results obtained through simulation are compared to other evolutionary optimization techniques surfaced in the recent state-of-the-art literature, including wavelet neural network model. The results presented in this paper demonstrate the potential of the proposed approach and show its effectiveness for electricity price forecasting.

Fouling analysis of a shell and tube heat exchanger using local linear wavelet neural network

A local linear wavelet neural network based model has been developed to predict the temperature differences on both the tube and shell side and the heat exchanger efficiency. This network replaces the straightforward weight by a local linear model. The working process of the proposed network can be viewed as to decompose the complex, nonlinear system into a set of locally active submodels and then smoothly integrate those submodels by their associated wavelet basis functions. For a given approximation or prediction problem with sufficient accuracy, the local linear models provide more power than a constant weight model as the dilation and translation parameters of LLWNN are randomly generated and optimized without predetermination. The closeness of the predicted results with the actual experimental results and higher accuracy with maximum error of 1.25% indicates that LLWNN can be used as a suitable tool for simulation of heat exchangers subjected to fouling.

Hybrid machine intelligence-based improved classifiers for multiple power signal disturbances

This work presents a new technique for the identification of multiple power quality disturbances in industrial power systems. This new technique is developed based on change detection filter (CDF) and machine intelligence techniques like the local linear radial basis function neural network (LLRBFNN) and decision tree (DT). The proposed change detection filter has been designed in one cycle back fashion for the multiple non–stationary transient power signal disturbances for localisation, and feature extraction. The extracted features are fed as inputs to either a LLRBFNN or a decision tree–based classifier (DTC) for characterisation of the power quality events. The performance of LLRBFNN and DTC are evaluated and compared with local linear wavelet neural network (LLWNN), wavelet neural network (WNN), radial basis function neural network (RBFNN), etc., to highlight the superior performance of the proposed classifiers.

A k Nearest Neighbor based Local Linear Wavelet Neural Network Model for On-line Short-term Traffic Volume Prediction

This paper presents a forecasting method called k nearest neighbor based local linear wavelet neural network (kNN-LLWNN) for the on-line, short-term prediction of five-minute traffic volumes at westbound of Interstate 64 in Hampton Road in Virginia. The method is based on combining k nearest neighbor (k-NN), with local linear wavelet neural network (LLWNN). The idea is to apply k-NN method to form the training dataset for LLWNN instead of taking the whole historical dataset for training. The proposed model is compared with k-NN, LLWNN and support vector regression (SVR) separately from prediction accuracy and running time two aspects. Experiments are conducted to decide the most appropriate parameters for the four models for the verification dataset. For the test dataset, the study's findings appear to confirm the hypothesis that, kNN-LLWNN performs comparable with LLWNN and SVR, and its running time is much lower than LLWNN and SVR because of the introduction of k-NN.

Application of Local Linear Wavelet Neural Network in Short Term Electric Load Forecasting

Application of Local Linear Wavelet Neural Network in Short Term Electric Load Forecasting

Local linear wavelet neural network based breast tumor classification using firefly algorithm

Breast cancer is the major cause of cancer deaths in women today and it is the most common type of cancer in women. This paper presents some experiments for classifying breast cancer tumor and proposes the use of firefly algorithm (FA) to improve the performance of Local linear wavelet neural network. This work in fact uses FA to optimize the parameters of local linear wavelet neural network. The experiments were conducted on extracted breast cancer data from University of Winconsin Hospital, Madison. The result has been compared with a wide range of classifiers to evaluate its performance. The evaluations show that the proposed approach is very robust, effective and gives better correct classification as compared to other classifiers.

A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices

This paper presents an integrated functional link interval type-2 fuzzy neural system (FLIT2FNS) for predicting the stock market indices. The hybrid model uses a TSK (Takagi–Sugano–Kang) type fuzzy rule base that employs type-2 fuzzy sets in the antecedent parts and the outputs from the Functional Link Artificial Neural Network (FLANN) in the consequent parts. Two other approaches, namely the integrated FLANN and type-1 fuzzy logic system and Local Linear Wavelet Neural Network (LLWNN) are also presented for a comparative study. Backpropagation and particle swarm optimization (PSO) learning algorithms have been used independently to optimize the parameters of all the forecasting models. To test the model performance, three well known stock market indices like the Standard's & Poor's 500 (S&P 500), Bombay stock exchange (BSE), and Dow Jones industrial average (DJIA) are used. The mean absolute percentage error (MAPE) and root mean square error (RMSE) are used to find out the performance of all the three models. Finally, it is observed that out of three methods, FLIT2FNS performs the best irrespective of the time horizons spanning from 1 day to 1 month.

Short-Term Load Forecasting using PSO Based Local Linear Wavelet Neural Network

Short-term load forecasting (STLF) plays an important role in the operational planning security functions of an energy management system. The short term load forecasting is aimed at predicting electric loads for a period of minutes, hours, days or week for the purpose of providing fundamental load profiles to the system. The work presented in this paper makes use of PSO based local linear wavelet neural networks (LLWNN) to find the electric load for a given period, with a certain confidence level. The results of the new method show significant improvement in the load forecasting process.

Local linear wavelet neural network for breast cancer recognition

Breast cancer is the major cause of cancer deaths in women today and it is the most common type of cancer in women. Many sophisticated algorithm have been proposed for classifying breast cancer data. This paper presents some experiments for classifying breast cancer tumor and proposes the use local linear wavelet neural network for breast cancer recognition by training its parameters using Recursive least square (RLS) approach to improve its performance. The difference of the local linear wavelet network with conventional wavelet neural network (WNN) is that the connection weights between hidden layer and output layer of conventional WNN are replaced by a local linear model. The result quality has been estimated and compared with other experiments. Results on extracted breast cancer data from University of Wisconsin Hospital Madison show that the proposed approach is very robust, effective and gives better classification.

Intelligent system based on local linear wavelet neural network and recursive least square approach for breast cancer classification

A new learning technique for local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of Error Back propagation and Recursive Least Square (RLS) is introduced for training the parameters of LLWNN. The variance and centers of LLWNN are updated using back propagation and weights are updated using Recursive Least Square (RLS). Results on extracted breast cancer data from University of Wisconsin Hospital Madison show that the proposed approach is very robust, effective and gives better classification.