Charles K. Ayo

Bio: Charles K. Ayo is an academic researcher from Covenant University. The author has contributed to research in topics: Government & The Internet. The author has an hindex of 17, co-authored 139 publications receiving 1751 citations. Previous affiliations of Charles K. Ayo include Trinity University.

Stock Price Prediction Using the ARIMA Model

Abstract: Stock price prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The autoregressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model. Published stock data obtained from New York Stock Exchange (NYSE) and Nigeria Stock Exchange (NSE) are used with stock price predictive model developed. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction and can compete favourably with existing techniques for stock price prediction.

Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction

Abstract: This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange. The empirical results obtained reveal the superiority of neural networks model over ARIMA model. The findings further resolve and clarify contradictory opinions reported in literature over the superiority of neural networks and ARIMA model and vice versa.

E-banking users’ behaviour: e-service quality, attitude, and customer satisfaction

Abstract: Purpose – The purpose of this paper is to investigate factors affecting e-banking usage based on electronic service (e-service) quality, attitude and customer satisfaction. Design/methodology/approach – A conceptual model to investigate factors that influence e-banking usage was developed based on review of existing literature. The model employed e-services quality variable, diffusion of innovation construct and self-efficacy to better reflect the users’ views of e-banking usage. Data collected from 254 e-banking users were used to test the model. The data were analysed based on PLS-SEM using SmartPLS 3.0. Findings – The result reveals that perceived e-service quality has a strong influence on customer satisfaction and use of e-banking, which means that greater quality of e-service has the potential to increase satisfaction and consequently result in to more use of e-banking. In this research findings, competence of e-service support staff, system availability, service portfolio, responsiveness and reliab...

The burden of road traffic crashes, injuries and deaths in Africa: a systematic review and meta-analysis.

Abstract: OBJECTIVE: To estimate the burden of road traffic injuries and deaths for all road users and among different road user groups in Africa. METHODS: We searched MEDLINE, EMBASE, Global Health, Google Scholar, websites of African road safety agencies and organizations for registry- and population-based studies and reports on road traffic injury and death estimates in Africa, published between 1980 and 2015. Available data for all road users and by road user group were extracted and analysed. We conducted a random-effects meta-analysis and estimated pooled rates of road traffic injuries and deaths. FINDINGS: We identified 39 studies from 15 African countries. The estimated pooled rate for road traffic injury was 65.2 per 100 000 population (95% confidence interval, CI: 60.8-69.5) and the death rate was 16.6 per 100 000 population (95% CI: 15.2-18.0). Road traffic injury rates increased from 40.7 per 100 000 population in the 1990s to 92.9 per 100 000 population between 2010 and 2015, while death rates decreased from 19.9 per 100 000 population in the 1990s to 9.3 per 100 000 population between 2010 and 2015. The highest road traffic death rate was among motorized four-wheeler occupants at 5.9 per 100 000 population (95% CI: 4.4-7.4), closely followed by pedestrians at 3.4 per 100 000 population (95% CI: 2.5-4.2). CONCLUSION: The burden of road traffic injury and death is high in Africa. Since registry-based reports underestimate the burden, a systematic collation of road traffic injury and death data is needed to determine the true burden. This article is available in the standard WHO languages (English, Arabic, Chinese, French, Russian, Spanish) at the following DOI: 10.2471/BLT.15.163121 Language: en

An Estimate of the Incidence of Prostate Cancer in Africa: A Systematic Review and Meta-Analysis

Abstract: Background Prostate cancer (PCa) is rated the second most common cancer and sixth leading cause of cancer deaths among men globally. Reports show that African men suffer disproportionately from PCa compared to men from other parts of the world. It is still quite difficult to accurately describe the burden of PCa in Africa due to poor cancer registration systems. We systematically reviewed the literature on prostate cancer in Africa and provided a continent-wide incidence rate of PCa based on available data in the region. Methods A systematic literature search of Medline, EMBASE and Global Health from January 1980 to June 2015 was conducted, with additional search of Google Scholar, International Association of Cancer Registries (IACR), International Agency for Research on Cancer (IARC), and WHO African region websites, for studies that estimated incidence rate of PCa in any African location. Having assessed quality and consistency across selected studies, we extracted incidence rates of PCa and conducted a random effects meta-analysis. Results Our search returned 9766 records, with 40 studies spreading across 16 African countries meeting our selection criteria. We estimated a pooled PCa incidence rate of 22.0 (95% CI: 19.93–23.97) per 100,000 population, and also reported a median incidence rate of 19.5 per 100,000 population. We observed an increasing trend in PCa incidence with advancing age, and over the main years covered. Conclusion Effective cancer registration and extensive research are vital to appropriately quantifying PCa burden in Africa. We hope our findings may further assist at identifying relevant gaps, and contribute to improving knowledge, research, and interventions targeted at prostate cancer in Africa.

Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting.

Abstract: Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long-range dependency coupling between output and input efficiently. Recent studies have shown the potential of Transformer to increase the prediction capacity. However, there are several severe issues with Transformer that prevent it from being directly applicable to LSTF, including quadratic time complexity, high memory usage, and inherent limitation of the encoder-decoder architecture. To address these issues, we design an efficient transformer-based model for LSTF, named Informer, with three distinctive characteristics: (i) a $ProbSparse$ self-attention mechanism, which achieves $O(L \log L)$ in time complexity and memory usage, and has comparable performance on sequences' dependency alignment. (ii) the self-attention distilling highlights dominating attention by halving cascading layer input, and efficiently handles extreme long input sequences. (iii) the generative style decoder, while conceptually simple, predicts the long time-series sequences at one forward operation rather than a step-by-step way, which drastically improves the inference speed of long-sequence predictions. Extensive experiments on four large-scale datasets demonstrate that Informer significantly outperforms existing methods and provides a new solution to the LSTF problem.

Deep learning networks for stock market analysis and prediction

Abstract: Deep learning networks are applied to stock market analysis and prediction.A comprehensive analysis with different data representation methods is offered.Five-minute intraday data from the Korean KOSPI stock market is used.The network applied to residuals of autoregressive model improves prediction.Covariance estimation for market structure analysis is improved with the network. We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data representation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using high-frequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methodsprincipal component analysis, autoencoder, and the restricted Boltzmann machineon the networks overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive network is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction.

A Comparison of ARIMA and LSTM in Forecasting Time Series

Abstract: Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with its many variations. In particular, ARIMA model has demonstrated its outperformance in precision and accuracy of predicting the next lags of time series. With the recent advancement in computational power of computers and more importantly development of more advanced machine learning algorithms and approaches such as deep learning, new algorithms are developed to analyze and forecast time series data. The research question investigated in this article is that whether and how the newly developed deep learning-based algorithms for forecasting time series data, such as "Long Short-Term Memory (LSTM)", are superior to the traditional algorithms. The empirical studies conducted and reported in this article show that deep learning-based algorithms such as LSTM outperform traditional-based algorithms such as ARIMA model. More specifically, the average reduction in error rates obtained by LSTM was between 84 - 87 percent when compared to ARIMA indicating the superiority of LSTM to ARIMA. Furthermore, it was noticed that the number of training times, known as "epoch" in deep learning, had no effect on the performance of the trained forecast model and it exhibited a truly random behavior.

Back propagation neural network with adaptive differential evolution algorithm for time series forecasting

Abstract: We propose a BPNN with adaptive differential evolution (ADE) for time series forecasting.ADE is used to search for global initial connection weights and thresholds of BPNN.The proposed ADE-BPNN is effective for improving forecasting accuracy. The back propagation neural network (BPNN) can easily fall into the local minimum point in time series forecasting. A hybrid approach that combines the adaptive differential evolution (ADE) algorithm with BPNN, called ADE-BPNN, is designed to improve the forecasting accuracy of BPNN. ADE is first applied to search for the global initial connection weights and thresholds of BPNN. Then, BPNN is employed to thoroughly search for the optimal weights and thresholds. Two comparative real-life series data sets are used to verify the feasibility and effectiveness of the hybrid method. The proposed ADE-BPNN can effectively improve forecasting accuracy relative to basic BPNN, autoregressive integrated moving average model (ARIMA), and other hybrid models.

The Performance of LSTM and BiLSTM in Forecasting Time Series

Abstract: Machine and deep learning-based algorithms are the emerging approaches in addressing prediction problems in time series. These techniques have been shown to produce more accurate results than conventional regression-based modeling. It has been reported that artificial Recurrent Neural Networks (RNN) with memory, such as Long Short-Term Memory (LSTM), are superior compared to Autoregressive Integrated Moving Average (ARIMA) with a large margin. The LSTM-based models incorporate additional “gates” for the purpose of memorizing longer sequences of input data. The major question is that whether the gates incorporated in the LSTM architecture already offers a good prediction and whether additional training of data would be necessary to further improve the prediction. Bidirectional LSTMs (BiLSTMs) enable additional training by traversing the input data twice (i.e., 1) left-to-right, and 2) right-to-left). The research question of interest is then whether BiLSTM, with additional training capability, outperforms regular unidirectional LSTM. This paper reports a behavioral analysis and comparison of BiLSTM and LSTM models. The objective is to explore to what extend additional layers of training of data would be beneficial to tune the involved parameters. The results show that additional training of data and thus BiLSTM-based modeling offers better predictions than regular LSTM-based models. More specifically, it was observed that BiLSTM models provide better predictions compared to ARIMA and LSTM models. It was also observed that BiLSTM models reach the equilibrium much slower than LSTM-based models.