Design Analysis and Implementation of Stock Market Forecasting System using Improved Soft Computing Technique

In this paper, a stock market prediction model was created utilizing artificial neural networks. Many people nowadays are attempting to predict future trends in bonds, currencies, equities, and stock markets. It is quite challenging for a capitalist and an industry to forecast changes in stock market prices. Due to the numerous economic, political, and psychological aspects at play, forecasting future value changes on the stock markets is quite challenging. In addition, stock market forecasting is a difficult endeavor because it relies on a wide range of known and unknown variables. Many approaches, including technical analysis, fundamental analysis, time series analysis, and statistical analysis are used to attempt to predict the share price; however, none of these methods has been demonstrated to be a consistently effective prediction tool. Artificial neural networks (ANNs), a subfield of artificial intelligence, are one of the most modern and promising methods for resolving financial issues, such as categorizing corporate bonds and anticipating stock market indexes and bankruptcy (AI). Artificial neural networks (ANN) are a prominent technology used to forecast the future of the stock market. In order to understand financial time series, it is often essential to extract relevant information from enormous data sets using artificial neural networks. An outcome prediction neural network with three layers is trained using the back propagation method. Analysis shows that ANN outperforms every other prediction technique now available to academics in terms of stock market price predictions. It is concluded that ANN is a useful technique for predicting stock market movements globally

Design and Modeling of Stock Market Forecasting Using Hybrid Optimization Techniques

In this paper, an artificial neural network-based stock market prediction model was developed. Today, a lot of individuals are making predictions about the direction of the bond, currency, equity, and stock markets. Forecasting fluctuations in stock market values is quite difficult for businesspeople and industries. Forecasting future value changes on the stock markets is exceedingly difficult since there are so many different economic, political, and psychological factors at play. Stock market forecasting is also a difficult endeavour since it depends on so many various known and unknown variables. There are several ways used to try to anticipate the share price, including technical analysis, fundamental analysis, time series analysis, and statistical analysis; however, none of these approaches has been shown to be a consistently reliable prediction tool. We built three alternative Adaptive Neuro-Fuzzy Inference System (ANFIS) models to compare the outcomes. The average of the tuned models is used to create an ensemble model. Although comparable applications have been attempted in the literature, the data set is extremely difficult to work with because it only contains sharp peaks and falls with no seasonality. In this study, fuzzy c-means clustering, subtractive clustering, and grid partitioning are all used. The experiments we ran were designed to assess the effectiveness of various construction techniques used to our ANFIS models. When evaluating the outcomes, the metrics of R-squared and mean standard error are mostly taken into consideration. In the experiments, R-squared values of over.90 are attained

Implementation of Discretisation and Correlation-based Feature Selection to Optimize Support Vector Machine in Diagnosis of Chronic Kidney Disease

This study aims to improve the accuracy of the classification algorithm for diagnosing chronic kidney disease. There are several models of data mining. In classification, the Support Vector Machine (SVM) algorithm is widely used by researchers worldwide. The data used is a chronic kidney disease dataset taken from the UCI machine learning repository. This data consists of 25 attributes and 11 numeric data attributes, and 14 negative attributes. To call continuously, discrete data is used. Meanwhile, data is selected using Correlation-based Feature Selection (CFS) to reduce irrelevant and redundant data. The research results by applying discretization and feature selection based on correlation for classification in the SVM algorithm with 10-fold cross-validation show an increase in accuracy of 0.5%. The classification of the vector machine support algorithm in the diagnosis of chronic kidney disease produces an accuracy of 99.25%, and after applying discretization and correlation-based feature selection, produces an accuracy of 99.75%. Implementation of discretion and correlation-based feature selection to optimize support vector machine for diagnosis of chronic kidney disease has increased accuracy by 0.5%. The proposed method is feasible as a method of diagnosing chronic kidney disease