No abstract

The stock market is considered to be a stochastic and challenging real-world environment, where the stock-price movements are affected by a considerable number of factors [1, 2]. Billions of structured and unstructured data are generated daily from the stock market around the globe, increasing the "volume", "velocity", "variety" and

No abstract

The economic growth of every nation is highly related to its electricity infrastructure, network, and availability since electricity has become the central part of everyday life in this modern world. Hence, the global demand for electricity for residential and commercial purposes has seen an incredible increase. On the other side, electricity prices keep fluctuating over the past years and not mentioning the inadequacy in electricity generation to meet global demand. As a solution to this, numerous studies aimed at estimating future electrical energy demand for residential and commercial purposes to enable electricity generators, distributors, and suppliers to plan effectively ahead and promote energy conservation among the users. Notwithstanding, load forecasting is one of the major problems facing the power industry since the inception of electric power. The current study tried to undertake a systematic and critical review of about seventy-seven (77) relevant previous works reported in academic journals over nine years (2010–2020) in electricity demand forecasting. Specifically, attention was given to the following themes: (i) The forecasting algorithms used and their fitting ability in this field, (ii) the theories and factors affecting electricity consumption and the origin of research work, (iii) the relevant accuracy and error metrics applied in electricity load forecasting, and (iv) the forecasting period. The results revealed that 90% out of the top nine models used in electricity forecasting was artificial intelligence based, with artificial neural network (ANN) representing 28%. In this scope, ANN models were primarily used for short-term electricity forecasting where electrical energy consumption patterns are complicated. Concerning the accuracy metrics used, it was observed that root-mean-square error (RMSE) (38%) was the most used error metric among electricity forecasters, followed by mean absolute percentage error MAPE (35%). The study further revealed that 50% of electricity demand forecasting was based on weather and economic parameters, 8.33% on household lifestyle, 38.33% on historical energy consumption, and 3.33% on stock indices. Finally, we recap the challenges and opportunities for further research in electricity load forecasting locally and globally.

AbstractPredicting stock-price remains an important subject of discussion among financial analysts and researchers. However, the advancement in technologies such as artificial intelligence and machine learning techniques has paved the way for better and accurate prediction of stock-price in recent years. Of late, Support Vector Machines (SVM) have earned popularity among Machine Learning (ML) algorithms used for predicting stock price. However, a high percentage of studies in algorithmic investments based on SVM overlooked the overfitting nature of SVM when the input dataset is of high-noise and high-dimension. Therefore, this study proposes a novel homogeneous ensemble classifier called GASVM based on support vector machine enhanced with Genetic Algorithm (GA) for feature-selection and SVM kernel parameter optimisation for predicting the stock market. The GA was introduced in this study to achieve a simultaneous optimal of the diverse design factors of the SVM. Experiments carried out with over eleven (11) years’ stock data from the Ghana Stock Exchange (GSE) yielded compelling results. The outcome shows that the proposed model (named GASVM) outperformed other classical ML algorithms (Decision Tree (DT), Random Forest (RF) and Neural Network (NN)) in predicting a 10-day-ahead stock price movement. The proposed (GASVM) showed a better prediction accuracy of 93.7% compared with 82.3% (RF), 75.3% (DT), and 80.1% (NN). It can, therefore, be deduced from the fallouts that the proposed (GASVM) technique puts-up a practical approach feature-selection and parameter optimisation of the different design features of the SVM and thus remove the need for the labour-intensive parameter optimisation.