Predicting the daily return direction of the stock market using hybrid machine learning algorithms

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.

A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

A Machine Learning Based Algorithm to Process Partial Shading Effects in PV Arrays

Solar energy is a widely used type of renewable energy.Photovoltaic arrays are used to harvest solar energy.The major goal,in harvesting the maximum possible power,is to operate the system at its maximum power point(MPP).If the irradiation conditions are uniform,the P-V curve of the PV array has only one peak that is called its MPP.But when the irradiation conditions are non-uniform,the P-V curve has multiple peaks.Each peak represents an MPP for a specific irradiation condition.The highest of all the peaks is called Global Maximum Power Point(GMPP).Under uniform irradiation conditions,there is zero or no partial shading.But the changing irradiance causes a shading effect which is called Partial Shading.Many conventional and soft computing techniques have been in use to harvest solar energy.These techniques perform well under uniform and weak shading conditions but fail when shading conditions are strong.In this paper,a new method is proposed which uses Machine Learning based algorithm called Opposition-Based-Learning(OBL)to deal with partial shading conditions.Simulation studies on different cases of partial shading have proven this technique effective in attaining MPP.

Estimating SVCV waterborne transmission and predicting experimental epidemic development:A modeling study using a machine learning approach

Viral infectious diseases significantly threaten the sustainability of freshwater fish aquaculture.The lack of studies on epidemic transmission patterns and mechanisms inhibits the development of containment strategies from the viewpoint of veterinary public health.This study raises an epidemic mathematical model considering water transmission with the aim of analyzing the transmission process more accurately.The basic reproduction number R0 was derived by the model parameter including the water transmission coefficient and was used for the analysis of the virus transmission.Spring viremia of carp virus(SVCV)and zebrafish were used as model viruses and animals,respectively,to conduct the transmission experiment.Transmission through water was achieved by connecting two aquarium tanks with a water channel but blocking the fish movement between the tanks.With the collected experimental data,we determined the optimal hybrid machine learning algorithm to analyze the transmission process using an established mathematical model.In addition,future transmission was predicted and validated using the epidemic model and an optimal algorithm.Finally,the sensitivity of model parameters and the simulations of R0 variation were performed based on the modified complex epidemic model.This study is of significance in providing theoretical guidance for minimizing R0 by manipulating model parameters with containment measures.More importantly,since the modified model and algorithm demonstrated better performance in handling freshwater fish transmission problems,this study advances the future application of transmissible disease modeling with larger datasets in freshwater fish aquaculture.

High resolution 3D nonlinear integrated inversion

The high resolution 3D nonlinear integrated inversion method is based on nonlinear theory. Under layer control, the log data from several wells （or all wells） in the study area and seismic trace data adjacent to the wells are input to a network with multiple inputs and outputs and are integratedly trained to obtain an adaptive weight function of the entire study area. Integrated nonlinear mapping relationships are built and updated by the lateral and vertical geologic variations of the reservoirs. Therefore, the inversion process and its inversion results can be constrained and controlled and a stable seismic inversion section with high resolution with velocity inversion, impedance inversion, and density inversion sections, can be gained. Good geologic effects have been obtained in model computation tests and real data processing, which verified that this method has high precision, good practicality, and can be used for quantitative reservoir analysis.