Financial Forecasting Using Evolutionary Computational Techniques

Financial forecasting or specially stock market prediction is one of the hottest field of research lately due to its commercial applications owing to high stakes and the kinds of attractive benefits that it has to offer. In this project we have analyzed various evolutionary computation algorithms for forecasting of financial data. The financial data has been taken from a large database and has been based on the stock prices in leading stock exchanges .We have based our models on data taken from Bombay Stock Exchange (BSE), S&P500 (Standard and Poor’s) and Dow Jones Industrial Average (DJIA). We have designed three models and compared those using historical data from the three stock exchanges. The models used were based on: 1. Radial Basis Function parameters updated by Particle swarm optimization. 2. Radial Basis Function parameters updated by Least Mean Square Algorithm. 3. FLANN parameters updated by Particle Swarm optimization. The raw input for the experiment is the historical daily open, close, high, low and volume of the concerned index. However the actual input to the model was the parameters derived from these data. The results of the experiment have been depicted with the aid of suitable curves where a comparative analysis of the various models is done on the basis on various parameters including error convergence and the Mean Average Percentage Error (MAPE). Key Words: Radial Basis Functions, FLANN, PSO, LM

Swarm intelligence and evolutionary computation approaches for 2D face recognition: a systematic review

A wide range of approaches for 2D face recognition (FR) systems can be found in the literature due to its high applicability and issues that need more investigation yet which include occlusion, variations in scale, facial expression, and illumination. Over the last years, a growing number of improved 2D FR systems using Swarm Intelligence and Evolutionary Computing algorithms have emerged. The present work brings an up-to-date Systematic Literature Review (SLR) concerning the use of Swarm Intelligence and Evolutionary Computation applied in 2D FR systems. Also, this review analyses and points out the key techniques and algorithms used and suggests some directions for future research

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Evolutionary Neuro-Computing Approaches to System Identification

System models are essentially required for analysis, controller design and future prediction. System identification is concerned with developing models of physical system. Although linear system identification got enriched with several useful classical methods, nonlinear system identification always remained active area of research due to the reason that most of the real world systems are nonlinear in nature and moreover, having non-unique models. Among the several conventional system identification techniques, the Volterra series, Hammerstein-Wiener and polynomial model identification involve considerable computational complexities. The other techniques based on regression models such as nonlinear autoregressive exogenous (NARX) and nonlinear autoregressive moving average exogenous (NARMAX), also suffer from dfficulty in choosing regressors

A Radial Basis Function Neural Network using biologically plausible activation functions

Este proyecto se centra en el diseño, la implementación y la evaluación de Redes Neuronales de Función de Base Radial (RBFNN), comparando el modelo gaussiano con una nueva versión que utiliza la función de activación Ricker. La forma de esta función ha sido observada en las señales de neuronas de distintas partes del cerebro humano, a menudo produciendo una señal negativa (inhibitoria) conocida como inhibición lateral. Se han desarrollado dos modelos de RBFNN, incorporando técnicas de Machine Learning (ML) y estadística como la regularización L2 y el algoritmo sigest para mejorar su rendimiento. También se implementan técnicas adicionales, como la estimación de un parámetro k sobredimensionado y la AIC backward selection, para mejorar la eficiencia. En este estudio, los modelos desarrollados se prueban con conjuntos de datos de diferente naturaleza, evaluando su rendimiento con datos sintéticos y realistas, y midiendo sus resultados con problemas de varios niveles de ruido y dificultad. Además, también se realiza una comparación de los modelos para observar qué RBFNN funciona mejor en determinadas condiciones, así como para analizar la diferencia en el número de neuronas y el parámetro de suavizado estimado. La evaluación experimental confirma la eficacia de los modelos RBFNN, proporcionando estimaciones precisas y demostrando su adaptabilidad con problemas de dificultad variable. El análisis comparativo revela que el modelo Ricker tiende a exhibir un rendimiento superior en presencia de altos niveles de ruido, mientras que ambos modelos tienen un rendimiento similar en condiciones de bajo ruido. Estos resultados sugieren la potencial influencia de la inhibición lateral, que podría ser explorada en más profundidad en futuros estudios.This project focuses on the design, implementation and evaluation of Radial Basis Function Neural Networks (RBFNN), comparing the gaussian model with a new version using the Ricker Wavelet activation function. The shape of this wavelet has been observed in the signals of neurons from different parts of the human brain, often producing a negative (inhibitory) signal known as lateral inhibition. Two RBFNN models have been developed, incorporating Machine Learning (ML) and statistical techniques such as L2 regularization and the sigest algorithm for improved performance. Additional techniques, such as estimating an oversized k parameter and using AIC backward selection, are implemented to enhance efficiency. In this study, the developed models are tested with datasets of different nature, evaluating their performance with synthetic and realistic data and measuring their results with problems of various levels of noise and difficulty. Furthermore, a comparison of the models is also made in order to observe which RBFNN performs better on certain conditions, as well as to analyze the difference in the number of neurons and the estimated smoothing parameter. The experimental evaluation confirms the effectiveness of the RBFNN models, yielding accurate estimations and demonstrating their adaptability to problems of varying difficulty. Comparative analysis reveals that the Ricker model tends to exhibit superior performance in the presence of high levels of noise, while both models perform similarly under low noise conditions. These results suggest the potential influence of lateral inhibition, which could be explored further in future studies

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Channel Equalization using GA Family

High speed data transmissions over communication channels distort the trans- mitted signals in both amplitude and phase due to presence of Inter Symbol Inter- ference (ISI). Other impairments like thermal noise, impulse noise and cross talk also cause further distortions to the received symbols. Adaptive equalization of the digital channels at the receiver removes/reduces the e®ects of such ISIs and attempts to recover the transmitted symbols. Basically an equalizer is an inverse ¯lter which is placed at the front end of the receiver. Its transfer function is inverse to the transfer function of the associated channel. The Least-Mean-Square (LMS), Recursive-Least-Square (RLS) and Multilayer perceptron (MLP) based adaptive equalizers aim to minimize the ISI present in the digital communication channel. These are gradient based learning algorithms and therefore there is possibility that during training of the equalizers, its weights do not reach to their optimum values due to ..

Multidimensional Particle Swarm Optimization for Machine Learning

Particle Swarm Optimization (PSO) is a stochastic nature-inspired optimization method. It has been successfully used in several application domains since it was introduced in 1995. It has been especially successful when applied to complicated multimodal problems, where simpler optimization methods, e.g., gradient descent, are not able to find satisfactory results. Multidimensional Particle Swarm Optimization (MD-PSO) and Fractional Global Best Formation (FGBF) are extensions of the basic PSO. MD-PSO allows searching for an optimum also when the solution dimensionality is unknown. With a dedicated dimensional PSO process, MD-PSO can search for optimal solution dimensionality. An interleaved positional PSO process simultaneously searches for the optimal solution in that dimensionality. Both the basic PSO and its multidimensional extension MD-PSO are susceptible to premature convergence. FGBF is a plug-in to (MD-)PSO that can help avoid premature convergence and find desired solutions faster. This thesis focuses on applications of MD-PSO and FGBF in different machine learning tasks.Multiswarm versions of MD-PSO and FGBF are introduced to perform dynamic optimization tasks. In dynamic optimization, the search space slowly changes. The locations of optima move and a former local optimum may transform into a global optimum and vice versa. We exploit multiple swarms to track different optima.In order to apply MD-PSO for clustering tasks, two key questions need to be answered: 1) How to encode the particles to represent different data partitions? 2) How to evaluate the fitness of the particles to evaluate the quality of the solutions proposed by the particle positions? The second question is considered especially carefully in this thesis. An extensive comparison of Clustering Validity Indices (CVIs) commonly used as fitness functions in Particle Swarm Clustering (PSC) is conducted. Furthermore, a novel approach to carry out fitness evaluation, namely Fitness Evaluation with Computational Centroids (FECC) is introduced. FECC gives the same fitness to any particle positions that lead to the same data partition. Therefore, it may save some computational efforts and, above all, it can significantly improve the results obtained by using any of the best performing CVIs as the PSC fitness function.MD-PSO can also be used to evolve different neural networks. The results of training Multilayer Perceptrons (MLPs) using the common Backpropagation (BP) algorithm and a global technique based on PSO are compared. The pros and cons of BP and (MD-)PSO in MLP training are discussed. For training Radial Basis Function Neural Networks (RBFNNs), a novel technique based on class-specific clustering of the training samples is introduced. The proposed approach is compared to the common input and input-output clustering approaches and the benefits of using the class-specific approach are experimentally demonstrated. With the class-specific approach, the training complexity is reduced, while the classification performance of the trained RBFNNs may be improved.Collective Network of Binary Classifiers (CNBC) is an evolutionary semantic classifier consisting of several Networks of Binary Classifiers (NBCs) trained to recognize a certain semantic class. NBCs in turn consist of several Binary Classifiers (BCs), which are trained for a certain feature type. Thanks to its topology and the use of MD-PSO as its evolution technique, incremental training can be easily applied to add new training items, classes, and/or features.In feature synthesis, the objective is to exploit ground truth information to transform the original low-level features into more discriminative ones. To learn an efficient synthesis for a dataset, only a fraction of the data needs to be labeled. The learned synthesis can then be applied on unlabeled data to improve classification or retrieval results. In this thesis, two different feature synthesis techniques are introduced. In the first one, MD-PSO is directly used to find proper arithmetic operations to be applied on the elements of the original low-level feature vectors. In the second approach, feature synthesis is carried out using one-against-all perceptrons. In the latter technique, the best results were obtained when MD-PSO was used to train the perceptrons.In all the mentioned applications excluding MLP training, MD-PSO is used together with FGBF. Overall, MD-PSO and FGBF are indeed versatile tools in machine learning. However, computational limitations constrain their use in currently emerging machine learning systems operating on Big Data. Therefore, in the future, it is necessary to divide complex tasks into smaller subproblems and to conquer the large problems via solving the subproblems where the use of MD-PSO and FGBF becomes feasible. Several applications discussed in this thesis already exploit the divide-and-conquer operation model

On Development of Some Soft Computing Based Multiuser Detection Techniques for SDMA–OFDM Wireless Communication System

Space Division Multiple Access(SDMA) based technique as a subclass of Multiple Input Multiple Output (MIMO) systems achieves high spectral efficiency through bandwidth reuse by multiple users. On the other hand, Orthogonal Frequency Division Multiplexing (OFDM) mitigates the impairments of the propagation channel. The combination of SDMA and OFDM has emerged as a most competitive technology for future wireless communication system. In the SDMA uplink, multiple users communicate simultaneously with a multiple antenna Base Station (BS) sharing the same frequency band by exploring their unique user specific-special spatial signature. Different Multiuser Detection (MUD) schemes have been proposed at the BS receiver to identify users correctly by mitigating the multiuser interference. However, most of the classical MUDs fail to separate the users signals in the over load scenario, where the number of users exceed the number of receiving antennas. On the other hand, due to exhaustive search mechanism, the optimal Maximum Likelihood (ML) detector is limited by high computational complexity, which increases exponentially with increasing number of simultaneous users. Hence, cost function minimization based Minimum Error Rate (MER) detectors are preferred, which basically minimize the probability of error by iteratively updating receiver’s weights using adaptive algorithms such as Steepest Descent (SD), Conjugate Gradient (CG) etc. The first part of research proposes Optimization Techniques (OTs) aided MER detectors to overcome the shortfalls of the CG based MER detectors. Popular metaheuristic search algorithms like Adaptive Genetic Algorithm (AGA), Adaptive Differential Evolution Algorithm (ADEA) and Invasive Weed Optimization (IWO), which rely on an intelligent search of a large but finite solution space using statistical methods, have been applied for finding the optimal weight vectors for MER MUD. Further, it is observed in an overload SDMA–OFDM system that the channel output phasor constellation often becomes linearly non-separable. With increasing the number of users, the receiver weight optimization task turns out to be more difficult due to the exponentially increased number of dimensions of the weight matrix. As a result, MUD becomes a challenging multidimensional optimization problem. Therefore, signal classification requires a nonlinear solution. Considering this, the second part of research work suggests Artificial Neural Network (ANN) based MUDs on thestandard Multilayer Perceptron (MLP) and Radial Basis Function (RBF) frameworks fo