Soft Computing Techniques for Stock Market Prediction: A Literature Survey

Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market

Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques

Wind Turbine condition monitoring can detect anomalies in turbine performance which have the potential to result in unexpected failure and financial loss. This study examines common Supervisory Control And Data Acquisition (SCADA) data over a period of 20 months for 21 pitch regulated 2.3 MW turbines and is presented in three manuscripts. First, power curve monitoring is targeted applying various types of Artificial Neural Networks to increase modeling accuracy. It is shown how the proposed method can significantly improve network reliability compared with existing models. Then, an advance technique is utilized to create a smoother dataset for network training followed by establishing dynamic ANFIS network. At this stage, designed network aims to predict power generation in future hours. Finally, a recursive principal component analysis is performed to extract significant features to be used as input parameters of the network. A novel fusion technique is then employed to build an advanced model to make predictions of turbines performance with favorably low errors

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

Detection of Anomalies and Novelties in Time Series with Self-Organizing Networks

This paper introduces the DANTE project: Detection of Anomalies and Novelties in Time sEries with self-organizing networks. The goal of this project is to evaluate several self-organizing networks in the detection of anomalies/novelties in dynamic data patterns. For this purpose, we first describe three standard clustering-based approaches which uses well-known self-organizing neural architectures, such as the SOM and the Fuzzy ART algorithms, and then present a novel approach based on the Operator Map (OPM) network. The OPM is a generalization of the SOM where neurons are regarded as temporal filters for dynamic patters. The OPM is used to build local adaptive filters for a given nonstationary time series. Non-parametric confidence intervals are then computed for the residuals of the local models and used as decision thresholds for detecting novelties/anomalies. Computer simulations are carried out to compare the performances of the aforementioned algorithms

Techniques for Stock Market Prediction: A Review

Stock market forecasting has long been viewed as a vital real-life topic in economics world. There are many challenges in stock market prediction systems such as the Efficient Market Hypothesis (EMH), Nonlinearity, complex, diverse datasets, and parameter optimization. A stock's value on the stock market fluctuates due to many factors like previous trends of the stock, the current news, twitter feeds, any online customer feedbacks etc. In this paper, the literature is critically analysed on approaches used for stock market prediction in terms of stock datasets, features used, evaluation metrics used, statistical, machine learning and deep learning techniques along with the directions for the future. The focus of this review is on trend and value prediction for stocks. Overall, 68 research papers have been considered for review from years 1998-2023. From the review, Indian stock market datasets are found to be most frequently used datasets. Evaluation metrics used commonly are accuracy and Mean Absolute Percentage Error. ARIMA is reported as the most used frequently statistical technique for stick market prediction. Long-Short Term Memory and Support Vector Machine are the commonly used algorithms in stock market prediction. The advantages and disadvantages of frequently used evaluation metrics, machine learning, deep learning and statistical approaches are also included in this survey

Intelligent energy management system - techniques and methods

ABSTRACT Our environment is an asset to be managed carefully and is not an expendable resource to be taken for granted. The main original contribution of this thesis is in formulating intelligent techniques and simulating case studies to demonstrate the significance of the present approach for achieving a low carbon economy. Energy boosts crop production, drives industry and increases employment. Wise energy use is the first step to ensuring sustainable energy for present and future generations. Energy services are essential for meeting internationally agreed development goals. Energy management system lies at the heart of all infrastructures from communications, economy, and society’s transportation to the society. This has made the system more complex and more interdependent. The increasing number of disturbances occurring in the system has raised the priority of energy management system infrastructure which has been improved with the aid of technology and investment; suitable methods have been presented to optimize the system in this thesis. Since the current system is facing various problems from increasing disturbances, the system is operating on the limit, aging equipments, load change etc, therefore an improvement is essential to minimize these problems. To enhance the current system and resolve the issues that it is facing, smart grid has been proposed as a solution to resolve power problems and to prevent future failures. This thesis argues that smart grid consists of computational intelligence and smart meters to improve the reliability, stability and security of power. In comparison with the current system, it is more intelligent, reliable, stable and secure, and will reduce the number of blackouts and other failures that occur on the power grid system. Also, the thesis has reported that smart metering is technically feasible to improve energy efficiency. In the thesis, a new technique using wavelet transforms, floating point genetic algorithm and artificial neural network based hybrid model for gaining accurate prediction of short-term load forecast has been developed. Adopting the new model is more accuracy than radial basis function network. Actual data has been used to test the proposed new method and it has been demonstrated that this integrated intelligent technique is very effective for the load forecast. Choosing the appropriate algorithm is important to implement the optimization during the daily task in the power system. The potential for application of swarm intelligence to Optimal Reactive Power Dispatch (ORPD) has been shown in this thesis. After making the comparison of the results derived from swarm intelligence, improved genetic algorithm and a conventional gradient-based optimization method, it was concluded that swam intelligence is better in terms of performance and precision in solving optimal reactive power dispatch problems