Stock-market prediction using machine-learning technique aims at developing effective and efficient models that can provide a better and higher rate of prediction accuracy. Numerous ensemble regressors and classifiers have been applied in stock market predictions, using different combination techniques. However, three precarious issues come in mind when constructing ensemble classifiers and regressors. The first concerns with the choice of base regressor or classifier technique adopted. The second concerns the combination techniques used to assemble multiple regressors or classifiers and the third concerns with the quantum of regressors or classifiers to be ensembled. Subsequently, the number of relevant studies scrutinising these previously mentioned concerns are limited. In this study, we performed an extensive comparative analysis of ensemble techniques such as boosting, bagging, blending and super learners (stacking). Using Decision Trees (DT), Support Vector Machine (SVM) and Neural Network (NN), we constructed twenty-five (25) different ensembled regressors and classifiers. We compared their execution times, accuracy, and error metrics over stock-data from Ghana Stock Exchange (GSE), Johannesburg Stock Exchange (JSE), Bombay Stock Exchange (BSE-SENSEX) and New York Stock Exchange (NYSE), from January 2012 to December 2018. The study outcome shows that stacking and blending ensemble techniques offer higher prediction accuracies (90–100%) and (85.7–100%) respectively, compared with that of bagging (53–97.78%) and boosting (52.7–96.32%). Furthermore, the root means square error (RMSE) recorded by stacking (0.0001–0.001) and blending (0.002–0.01) shows a better fit of ensemble classifiers and regressors based on these two techniques in market analyses compared with bagging (0.01–0.11) and boosting (0.01–0.443). Finally, the results undoubtedly suggest that an innovative study in the domain of stock market direction prediction ought to include ensemble techniques in their sets of algorithms.

/pdf/a-comprehensive-evaluation-of-ensemble-learning-for-stock-14pcf72a0y.pdf

A comprehensive evaluation of ensemble learning for stock-market prediction

Hybrid structures in time series modeling and forecasting: A review

Combination of short-term load forecasting models based on a stacking ensemble approach

Smart buildings are equipped with sensors that allow monitoring a range of building systems including heating and air conditioning, lighting and the general electric energy consumption. Thees data can then be stored and analyzed. The ability to use historical data regarding electric energy consumption could allow improving the energy efficiency of such buildings, as well as help to spot problems related to wasting of energy. This problem is even more important when considering that buildings are some of the largest consumers of energy. In this paper, we are interested in forecasting the energy consumption of smart buildings, and, to this aim, we propose a comparative study of different forecasting strategies that can be used to this aim. To do this, we used the data regarding the electric consumption registered by thirteen buildings located in a university campus in the south of Spain. The empirical comparison of the selected methods on the different data showed that some methods are more suitable than others for this kind of problem. In particular, we show that strategies based on Machine Learning approaches seem to be more suitable for this task.

https://www.mdpi.com/1996-1073/12/10/1934/pdf

A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings

Time-series production forecasting method based on the integration of Bidirectional Gated Recurrent Unit (Bi-GRU) network and Sparrow Search Algorithm (SSA)

In the real-life, time-series data comprise a complicated pattern, hence it may be challenging to increase prediction accuracy rates by using machine learning and conventional statistical methods as single learners. This research outlines and investigates the Stacking Multi-Learning Ensemble (SMLE) model for time series prediction problem over various horizons with a focus on the forecasts accuracy, directions hit-rate, and the average growth rate of total oil demand. This investigation presents a flexible ensemble framework in light of blend heterogeneous models for demonstrating and forecasting nonlinear time series. The proposed SMLE model combines support vector regression (SVR), backpropagation neural network (BPNN), and linear regression (LR) learners, the ensemble architecture consists of four phases: generation, pruning, integration, and ensemble prediction task. We have conducted an empirical study to evaluate and compare the performance of SMLE using Global Oil Consumption (GOC). Thus, the assessment of the proposed model was conducted at single and multistep horizon prediction using unique benchmark techniques. The final results reveal that the proposed SMLE model outperforms all the other benchmark methods listed in this study at various levels such as error rate, similarity, and directional accuracy by 0.74%, 0.020%, and 91.24%, respectively. Therefore, this study demonstrates that the ensemble model is an extremely encouraging methodology for complex time series forecasting.

Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model

Currently, predicting time series utilizes as interesting research area for temporal mining aspects. Financial Time Series (FTS) delineated as one of the most challenging tasks, due to data characteristics is devoid of linearity, stationary, noisy, high degree of uncertainty and hidden relations. Several singles' models proposed using both statistical and data mining approaches powerless to deal with these issues. The main objective of this study to propose a hybrid model, using additive and linear regression methods to combine linear and non-linear models. However, three models are investigated namely ARIMA, EXP, and ANN. Firstly, those models are feeding by exchange rate data set (SDG-EURO). Then, the arithmetical outcome of each model examined as benchmark models and set of aforementioned hybrid models in related literature. Results showed the superiority in hybrid model on all other investigated models based on 0.82% MAPE error's measure for accuracy. Based on the results of this study, we can conclude that further experiments desirable to estimate the weights for accurate combination method and more models essential to be surveyed in the areas of series prediction.

/pdf/hybrid-forecasting-scheme-for-financial-time-series-data-3s3f5ftgpw.pdf

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Financial time-series data have non-linear and uncertain behavior which changes across the time. Therefore, the need to solve non-linear, time-variant problems has been growing rapidly. Traditional models such as statistical and data mining approach unable to cope with these issues. The main objective of this study to combine forecasts from the autoregressive integrated moving average model, exponential (EXP) model, and the multi-layers perceptron (MLP) in a novel hybrid model. The analysis was based on financial data of Sudanese pound/EURO exchange rate in Sudan. In this case, simple additive combination and weight combination methods are used in combining linear and non-linear models to produce hybrid forecast. Comparison between benchmark models and hybrid indicates that the hybrid model offers more accurate forecasts with reduced mean-absolute percentage error of around 0.82% for all models over all forecasting horizons. Moreover, the results recommend that the non-linear method can be applicable to an alternate to linear combining methods to accomplish better forecasting accuracy. On the basis of the results of this study, the authors can conclude that further experiments to estimate the weight of the combination methods and more models essential to be surveyed so as to explore innovative concerns in series prediction.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/joe.2017.0337

Modelling and optimisation of effective hybridisation model for time-series data forecasting

Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements. Software-defined networking (SDN) is a networking concept that suggests the segregation of a network data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our framework.

/pdf/resource-aware-network-topology-management-framework-2bav552gnb.pdf

Resource-Aware Network Topology Management Framework

https://www.preprints.org/manuscript/201905.0174/v1/download

Nashat T. AL-Jallad

Papers

Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model

Hybrid Forecasting Scheme for Financial Time-Series Data using Neural Network and Statistical Methods

Modelling and optimisation of effective hybridisation model for time-series data forecasting

Resource-Aware Network Topology Management Framework

Resource-Aware Network Topology Management Framework