Automated Stock Trading Algorithm Using Neural Networks

This thesis evaluates the utility of Artificial Neural Networks (ANNs) applied to financial market and macroeconomic forecasting. In application, ANNs are evaluated in comparison to traditional forecasting models to evaluate if their nonlinear and adaptive properties yield superior forecasting performance in terms of robustness and accuracy. Furthermore, as ANNs are data-driven models, an emphasis is placed on the data collection stage by compiling extensive candidate input variable pools, a task frequently underperformed by prior research. In evaluating their performance, ANNs are applied to the domains of: exchange rate forecasting, volatility forecasting, and macroeconomic forecasting. Regarding exchange rate forecasting, ANNs are applied to forecast the daily logarithmic returns of the EUR/USD over a short-term forecast horizon of one period. Initially, the analytic method of Technical Analysis (TA) and its sub-section of technical indicators are utilized to compile an extensive candidate input variable pool featuring standard and advanced technical indicators measuring all technical aspects of the EUR/USD time series. The candidate input variable pool is then subjected to a two-stage Input Variable Selection (IVS) process, producing an informative subset of technical indicators to serve as inputs to the ANNs. A collection of ANNs is then trained and tested on the EUR/USD time series data with their performance evaluated over a 5-year sample period (2012 to 2016), reserving the last two years for out of sample testing. A Moving Average Convergence Divergence (MACD) model serves as a benchmark with the in-sample and out-of-sample empirical results demonstrating the MACD is a superior forecasting model across most forecast evaluation metrics. For volatility forecasting, ANNs are applied to forecast the volatility of the Nikkei 225 Index over a short-term forecast horizon of one period. Initially, an extensive candidate input variable pool is compiled consisting of implied volatility models and historical volatility models. The candidate input variable pool is then subjected to a two-stage IVS process. A collection of ANNs is then trained and tested on the Nikkei 225 Index time series data with their performance evaluated over a 4-year sample period (2014 to 2017), reserving the last year for out-of-sample testing. A GARCH (1,1) model serves as a benchmark with the out-of-sample empirical results finding the GARCH (1,1) model to be the superior volatility forecasting model. The research concludes with ANNs applied to macroeconomic forecasting, where ANNs are applied to forecast the monthly per cent-change in U.S. civilian unemployment and the quarterly per cent-change in U.S. Gross Domestic Product (GDP). For both studies, an extensive candidate input variable pool is compiled using relevant macroeconomic indicator data sourced from the Federal Bank of St Louis. The candidate input variable pools are then subjected to a two-stage IVS process. A collection of ANNs is trained and tested on the U.S. unemployment time series data (UNEMPLOY) and U.S. GDP time series data. The sample periods are (1972 to 2017) and (1960 to 2016) respectively, reserving the last 20% of data for out of sample testing. In both studies, the performance of the ANNs is benchmarked against a Support Vector Regression (SVR) model and a Naïve forecast. In both studies, the ANNs outperform the SVR benchmark model. The empirical results demonstrate that ANNs are superior forecasting models in the domain of macroeconomic forecasting, with the Modular Neural Network performing notably well. However, the empirical results question the utility of ANNs in the domains of exchange rate forecasting and volatility forecasting. A MACD model outperforms ANNs in exchange rate forecasting both in-sample and out-of-sample, and a GARCH (1,1) model outperforms ANNs in volatility forecasting.

This research reports application of artificial neural network (ANN) in investigation and optimisation of boron adsorption capacity in aqueous solution using amidoxime-modified poly(acrylonitrile-<i>co</i>-acrylic acid) (AO-modified poly(AN-<i>co</i>-AA)). Both feed-forward and recurrent ANN have been utilized to predict the adsorption potential of synthesised polymer. Three operational parameters, which are adsorbent dosage, initial pH and initial boron concentration during adsorption process were designed to study their effects on the removal capacity. The ANN was trained from experimental data and serviced to optimize, develop and create various prediction models in the process of boron adsorption by AO-modified poly(AN-<i>co</i>-AA). Among several models, radial basis function (RBF) with orthogonal least square (OLS) algorithm displayed good prediction on boron adsorption capacity with mean square error (MSE) and coefficient of determination (R<sup>2</sup>) at 0.000209 and 0.9985, respectively. With desirable the MSE and R<sup>2</sup> values, ANN worked as a promising prediction tool that was able to generate good estimate. The simulated maximum adsorption capacity of the synthesized polymer is 15.23 ± 1.05 mg boron/g adsorbent. Besides, from the results of ANN, the AO-modified poly(AN-<i>co</i>-AA) was proven to be a potential adsorbent for the removal of boron in wastewater treatment.

An optimized model using LSTM network for demand forecasting

38 - Artificial Neural Network Applications in Power Electronics and Electrical Drives

Artificial neural network (ANN) is a simplified model of the biological nervous system consisting of nerve cells or neurons. The application of ANN to food process engineering is relatively novel. ANN had been employed in diverse applications like food safety and quality analyses, food image analysis, and modeling of various thermal and non-thermal food-processing operations. ANN has the ability to map nonlinear relationships without any prior knowledge and predicts responses even with incomplete information. Every neural network possesses data in the form of connection weights interconnecting lines between the input to hidden layer neurons and weights of hidden to output layer neurons, which has a significant role in predicting the output data. The applications of ANN in different unit operations in food processing were described that includes theoretical developments using intelligent characteristics for adaptability, automatic learning, classification, and prediction. The parallel architecture of ANN resulted in a fast response and low computational time making it suitable for application in real-time systems of different food process operations. The predicted responses obtained by the ANN model exhibited high accuracy due to lower relative deviation and root mean squared error and higher correlation coefficient. This paper presented the various applications of ANN for modeling nonlinear food engineering problems. The application of ANN in the modeling of the processes such as extraction, extrusion, drying, filtration, canning, fermentation, baking, dairy processing, and quality evaluation was reviewed. HIGHLIGHTS 1. This paper discusses application of ANN in different emerging trends in food process. 2. Application of ANN to develop non-linear multivariate modeling is illustrated. 3. ANNs have been shown to be useful tool for prediction of outcomes with high accuracy. 4. ANN resulted in fast response making it suitable for application in real time systems.

Computer systems based on artificial neural networks, often known as neural networks or neural nets, are modeled after the organic neural networks seen in animal brains. Artificial neurons are a group of interconnected units or nodes that serve as the foundation of an ANN and are meant to approximate the function of biological brain neurons. This article contains a survey of practical uses for neural networks. It offers a taxonomy of Artificial Neural Networks (ANNs), in-forms the reader of recent and upcoming developments in ANN applications research, and highlights research areas of interest. This paper also discusses the difficulties, contributions, comparative effectiveness, and important methodologies in ANN applications. The study examines a wide range of ANN applications in numerous disciplines, including computing, science, engineering, medicine, the environment, agriculture, mining, technology, climate, business, and the arts similar to nanotechnology. This study analyses performance assesses ANN contributions and criticizes techniques. The study discovered that artificial neural networks with feedforward and feedback propagation do well when applied to solving human problems. Therefore, based on data analysis characteristics such as accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and efficiency, we presented feed-forward and feedback propagation ANN models for research centers. A computational model known as an artificial neural network (ANN) is composed of many processing elements that accept inputs and produce outputs by their specified activation functions. This article will make other articles in this computer magazine easier to grasp for those who know little or nothing about ANNs. We go over the reasons for creating ANNs, the fundamentals of a biological neuron and an artificial computer model, network designs, learning mechanisms, and some of the most widely used ANN models. A successful ANN character recognition application brings us to a close. EDAS Evaluation Based on Distance from Average Solution method for Notebook(n1), Notebook(n2), Notebook(n3), Notebook(n4), Notebook(n5). Notebook (n1), Notebook (n2), Notebook (n3), Notebook (n4), Notebook (n5). Speed (MHz), RAM (Mbytes), Display (inches), Price (Euro). Notebook (n5) has the highest rank whereas Notebook (n1) has the lowest rank. The need for hybrid systems, Optical Neural Networks, EDAS Method.

This article discusses the applications of Artificial Neural Network (ANN) and Fuzzy Logic Control (FLC) strategies in a control context of a grid connected Wind Energy Conversion System (WECS), using a Doubly-Fed Induction Generator (DFIG). To rigorously explore the performance of the controllers, a systemic approach based on an ideal model and a nominal model of the wind turbine is used for the comparative analysis between PI, FLC and ANN. It turns out that the PI is less efficient than ANN and FLC. It turns out that the PI is less efficient than ANN and FLC. Whereas, for a high level of control, the FLC has better performance than the ANN. For a low level, the performance of the ANN is very slightly superior to the FLC. In addition, one notes a good rejection of disturbance and a good robustness with the FLC compared to the ANN. The latter has a Total Harmonic Distortion (THD) and cos ϕ ≈ 1 slightly better than the FLC. In short, the ANN and FLC present great advantages for the WECS and the results obtained are satisfactory. The platform used for modeling and simulation studies is MATLAB/Simulink.KeywordsWind Energy Conversion SystemFuzzy Logic Control (FLC)Artificial Neural Network (ANN)DFIGPower grid

Accurate load forecasting is essential for reliable and efficient operation of power systems. Traditional forecasting methods often struggle with capturing complex nonlinear patterns in load data. Artificial neural networks (ANNs) have emerged as a promising alternative due to their ability to learn complex relationships from historical data (Syed et al. in IEEEA 9:54992–55008, 2021. https://doi.org/10.1109/ACCESS.2021.3071654). This study investigates the potential of ANNs for short-term peak load forecasting in a 150 kV power system in Semarang, Indonesia. The study examines the impact of different input variables, including historical peak load, minimum load, population, and energy production, on forecasting accuracy. Several ANN architectures are trained and evaluated using mean absolute percentage error (MAPE) and mean squared error (MSE) metrics as reported by Demuth and De Jesús (neural network design). The results indicate that ANNs can achieve high accuracy in predicting peak load, with MAPE values below 10%. The study also demonstrates the importance of carefully selecting input variables and training parameters for optimal model performance. The findings highlight the potential of ANNs for improving load forecasting accuracy in power systems, contributing to enhanced grid reliability and operational efficiency. The findings of this study contribute to a deeper understanding of the application of ANNs in power system load forecasting. They demonstrate the potential of ANNs to achieve high accuracy and provide valuable insights into the factors influencing model performance. The findings are relevant for power system operators, researchers, and policymakers working to improve grid reliability and efficiency as reported by Prabha Kundur and Malik (Power System Stability and Control, McGraw-Hill Education, New York, 2022. https://www.accessengineeringlibrary.com/content/book/9781260473544).

The artificial neural network could probably be the complete solution in recent decades, widely used in many applications. This chapter is devoted to the major applications of artificial neural networks and the importance of the e-learning application. It is necessary to adapt to the new intelligent e-learning system to personalize each learner. The result focused on the importance of using neural networks in possible applications and its influence on the learner's progress with the personalization system. The number of ANN applications has considerably increased in recent years, fueled by theoretical and applied successes in various disciplines. This chapter presents an investigation into the explosive developments of many artificial neural network related applications. The ANN is gaining importance in various applications such as pattern recognition, weather forecasting, handwriting recognition, facial recognition, autopilot, etc. Artificial neural network belongs to the family of artificial intelligence with fuzzy logic, expert systems, vector support machines.

Ecological Applications of Fuzzy Logic.- Ecological Applications of Qualitative Reasoning.- Ecological Applications of Non-supervised Artificial Neural Networks.- Ecological Applications of Genetic Algorithms.- Ecological Applications of Evolutionary Computation.- Ecological Applications of Adaptive Agents.- Bio-Inspired Design of Computer Hardware by Self-Replicating Cellular Automata.- Prediction and Elucidation of Stream Ecosystems.- Development and Application of Predictive River Ecosystem Models Based on Classification Trees and Artificial Neural Networks.- Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks.- Non-linear Approach to Grouping, Dynamics and Organizational Informatics of Benthic Macroinvertebrate Communities in Streams by Artificial Neural Networks.- Elucidation of Hypothetical Relationships between Habitat Conditions and Macroinvertebrate Assemblages in Freshwater Streams by Artificial Neural Networks.- Prediction and Elucidation of River Ecosystems.- Prediction and Elucidation of Population Dynamics of the Blue-green Algae Microcystis aeruginosa and the Diatom Stephanodiscus hantzschii in the Nakdong River-Reservoir System (South Korea) by a Recurrent Artificial Neural Network.- An Evaluation of Methods for the Selection of Inputs for an Artificial Neural Network Based River Model.- Utility of Sensitivity Analysis by Artificial Neural Network Models to Study Patterns of Endemic Fish Species.- Prediction and Elucidation of Lake and Marine Ecosystems.- A Comparison between Neural Network Based and Multiple Regression Models for Chlorophyll-a Estimation.- Artificial Neural Network Approach to Unravel and Forecast Algal Population Dynamics of Two Lakes Different in Morphometry and Eutrophication.- Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication.- Multivariate Time Series Prediction of Marine Zooplankton by Artificial Neural Networks.- Classification of Fish Stock-Recruitment Relationships in Different Environmental Regimes by Fuzzy Logic with Bootstrap Re-sampling Approach.- Computational Assemblage of Ordinary Differential Equations for Chlorophyll-a Using a Lake Process Equation Library and Measured Data of Lake Kasumigaura.- Classification of Ecological Images at Micro and Macro Scale.- Identification of Marine Microalgae by Neural Network Analysis of Simple Descriptors of Flow Cytometric Pulse Shapes.- Age Estimation of Fish Using a Probabilistic Neural Network.- Pattern Recognition and Classification of Remotely Sensed Images by Artificial Neural Networks.

This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN's MSE values are 40 times smaller than the LR's. The test data R value from ANN is 0.9931; thus indicating a high confidence level.

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. The ANN has recently been applied in process control, identification, diagnostics, character recognition, sensory prediction, robot vision, and forecasting. In Textiles and Clothing industries, it involves the interaction of a large number of variables. Because of the high degree of variability in raw materials, multistage processing and a lack of precise control on process parameters, the relation between such variables and the product properties is relied on the human knowledge but it is not possible for human being to remember all the details of the process-related data over the years. As the computing power has substantially improved over last decade, the ANN is able to learn such datasets to reveal the unknown relation between various variables effectively. Therefore, the application of ANN is more widespread in textiles and clothing industries over last decade. In this chapter, it aims to review current application of ANN in textiles and clothing industries over last decade. Based on literature reviews, the challenges encountered by ANN used in the industries will be discussed and the potential future application of ANN in the industries will also be addressed. The structure of this chapter comprises of seven sections. The first section includes background of ANN, importance of ANN in textiles and clothing and the arrangement of this chapter. In forthcoming three sections, they include review of applications of ANN in fibres and yarns, in chemical processing, and in clothing over last decade. Afterwards, challenges encountered by ANN used in textiles and clothing industries will be discussed and potential future application of ANN in textiles and clothing industries will be addressed in last section.

Applications of artificial neural networks in the field of genome research will be reviewed and some more recent developments in neural network research relevant for future applications will be surveyed. The basic definitions for artificial neural networks and neural learning algorithms will be introduced. The applications range from the recognition of translation initiation sites in nucleic acid sequences, the recognition of splice junctions and exons/ introns in mRNA, the detection of uncommon sequences in cDNA, to the prediction of secondary and tertiary structures of proteins from the amino acid sequence, the detection of structural motifs in protein sequences and the classification of protein sequences into functional families. Most applications employ multilayer feedforward networks trained supervised with the backpropagation learning algorithm or self-organising Kohonen maps adapted unsupervised for feature extraction. The most promising developments in neural network research usable in all mentioned applications are new modular network architectures with more problem-tailored connection topologies such as linked receptive fields and recurrent networks with short-term memory capable of modelling any dynamical system using only inductive learning.

The paper deals with the applications of artificial neural networks in the control of the DC drive. In the paper three control structures are discussed. The first control structure uses a conventional PI controller. The second structure uses a neural network predictive control. The last structure is a sensorless control of the DC drive using feedforward neural network. The DC drives were simulated in program Matlab with Simulink toolbox. The main goal was to find the simplest neural network structures with minimum number of neurons, but simultaneously good control characteristics are required. Despite used neural networks, which are very simple, it was achieved satisfactory results.KeywordsArtificial Neural NetworkArtificial Neural Network ModelModel Predictive ControlDirect Torque ControlInduction Motor DriveThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

NOVEL STAGING TOOL FOR LOCALIZED PROSTATE CANCER: A PILOT STUDY USING GENETIC ADAPTIVE NEURAL NETWORKS