Pearson Correlation Coefficient-based performance enhancement of Vanilla Neural Network for stock trend prediction

Thakkar, Ankit; Patel, Dhaval; Shah, Preet

doi:10.1007/s00521-021-06290-2

Cited by 33 publications

(7 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Statistical analyses were performed using SPSS software (version 18.0). Measurement data were expressed as mean ± SD (min-max), and Pearson correlation coefficient ( 8 ) was used for the correlation analysis. Values of p < 0.05 were considered to indicate a statistically significant difference.…”

Section: Methodsmentioning

confidence: 99%

An artificial neural network model based on standing lateral radiographs for predicting sitting pelvic tilt in healthy adults

Zhao

et al. 2022

Front. Surg.

View full text Add to dashboard Cite

BackgroundSpinopelvic motion, the cornerstone of the sagittal balance of the human body, is pivotal in patient-specific total hip arthroplasty.PurposeThis study aims to develop a novel model using back propagation neural network (BPNN) to predict pelvic changes when one sits down, based on standing lateral spinopelvic radiographs.MethodsYoung healthy volunteers were included in the study, 18 spinopelvic parameters were taken, such as pelvic incidence (PI) and so on. First, standing parameters correlated with sitting pelvic tilt (PT) and sacral slope (SS) were identified via Pearson correlation. Then, with these parameters as inputs and sitting PT and SS as outputs, the BPNN prediction network was established. Finally, the prediction results were evaluated by relative error (RE), prediction accuracy (PA), and normalized root mean squared error (NRMSE).ResultsThe study included 145 volunteers of 23.1 ± 2.3 years old (M:F = 51:94). Pearson analysis revealed sitting PT was correlated with six standing measurements and sitting SS with five. The best BPNN model achieved 78.48% and 77.54% accuracy in predicting PT and SS, respectively; As for PI, a constant for pelvic morphology, it was 95.99%.DiscussionIn this study, the BPNN model yielded desirable accuracy in predicting sitting spinopelvic parameters, which provides new insights and tools for characterizing spinopelvic changes throughout the motion cycle.

show abstract

Section: Methodsmentioning

confidence: 99%

An artificial neural network model based on standing lateral radiographs for predicting sitting pelvic tilt in healthy adults

Zhao

et al. 2022

Front. Surg.

View full text Add to dashboard Cite

show abstract

“…However, some studies concentrate on extracting the multi-scale information of financial time-series, such as combining shortterm market features with long-term temporal features to describe time-series more precisely [20]. Cui [22]. In 2023, they proposed coefficient of variation (CV)-based feature selection for stock prediction [23].…”

Section: Related Workmentioning

confidence: 99%

A Novel Convolutional Neural Networks for Stock Trading Based on DDQN Algorithm

Cui

Huang²,

et al. 2023

IEEE Access

View full text Add to dashboard Cite

In deep learning based stock trading strategy models, most of the research just use simple convolutional neural networks (CNN) to process stock data. But task-specific neural network structures have been proposed extensively, and their effectiveness has been demonstrated in computer vision (CV) and natural language processing (NLP) tasks. In this paper, we proposed a multi-scale convolutional neural feature extraction network (MS-CNN) for stock data, which can better extract stock trend features and thus make better decisions. The network structure inspired by the human stock trading model: in human behavior, we do not only look at a single set of stock data, but rather combine all the stock data, such as opening, closing, and trading volume, to make a comprehensive judgment. And humans will consider the current stock trend on different time scales, such as 3-Day Line and 5-Day Line. This is consistent with the two-dimensional convolution kernels commonly used in CV tasks, so we used convolution kernels of 3×3 and 5×5 in the network with two-dimensional convolution size and constructed a novel network structure for stock data. With double deep Q networks (DDQN) algorithm, we get the best performance for our network. The experimental results show that we can obtain high yield on the datasets of Dow Jones (DJI), AAPLE (AAPL), and General Electric (GE).

show abstract

“…The different correlation measures have been vastly applied in different fields, such as in finance, neurophysiology, meteorology, biology and engineering. Applications include the identification of genomic associations [ 89 , 90 ], the examination of the association of proteins in the pathogenesis of Parkinson’s disease [ 91 ], noise reduction [ 92 , 93 ], identification of disease-specific biomarker genes [ 94 ], multimodal image registration [ 95 ], portfolio optimization [ 96 , 97 ], investment decisions [ 98 ], wind power combination prediction [ 99 ], genetic interactions [ 100 ], artificial neural network model development that concerns water treatment plants [ 101 ], testing tourism economies and islands’ resilience to the global financial crisis [ 102 ], electroencephalograms (EEG) analysis [ 103 ], the study of financial markets [ 104 , 105 ], recognizing multiple positive emotions by analyzing brain activities [ 106 ], identifying meteorological parameters that play a major role in the transmission of infectious diseases such as COVID-19 [ 107 ] and stock trend prediction [ 108 , 109 ].…”

Section: Non-directional Connectivity Measuresmentioning

confidence: 99%

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

Papana

2021

Entropy

View full text Add to dashboard Cite

The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.

show abstract

Pearson Correlation Coefficient-based performance enhancement of Vanilla Neural Network for stock trend prediction

Cited by 33 publications

References 37 publications

An artificial neural network model based on standing lateral radiographs for predicting sitting pelvic tilt in healthy adults

An artificial neural network model based on standing lateral radiographs for predicting sitting pelvic tilt in healthy adults

A Novel Convolutional Neural Networks for Stock Trading Based on DDQN Algorithm

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

Contact Info

Product

Resources

About