Integration of Principal Component Analysis and Recurrent Neural Network to Forecast the Stock Price of Casablanca Stock Exchange

Berradi, Zahra; Lazaar, Mohamed

doi:10.1016/j.procs.2019.01.008

Cited by 75 publications

(24 citation statements)

References 20 publications

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“…The activation function used for PCA-DNN model is softsign activation function, as stated in Eq. (15).…”

Section: = ∑ | | =1mentioning

confidence: 99%

“…To improve the prediction accuracy of machine learning (ML) algorithms, Principal Component Analysis (PCA) has been used for reduction of high dimensional features. For example, PCA was used for dimensionality reduction of stock [13][14][15], air quality [16,17] and building natural period [18] dataset. In these applications, the feature space was first projected into lower subspace, while preserving variance of dataset using a simpler network architecture.…”

Section: Introductionmentioning

confidence: 99%

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House price prediction: A data-centric aspect approach on performance of combined principal component analysis with deep neural network model

Mostofi¹,

Toğan²,

Başağa³

2021

JCEMI

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High dimensionality and skewness are two intrinsic characteristics of real estate dataset that affects the price prediction accuracy of deep neural network (DNN). The objective of this study is to investigate the effect of skewness in prediction accuracy of combined principal component analysis (PCA) with DNN (PCA-DNN) model. This research follows a threefold approach over a high dimensional and positively skewed real estate price dataset. Firstly, data distribution is to conform with normality using three conventional skewness reduction techniques, namely as square root transformation (SRT), cube root transformation (CRT), and logarithmic transformation (LT) methods. Secondly, the high dimensionality of original, SRT, CRT and LT skewed datasets are to be reduced using PCA. Thirdly, price prediction accuracy of PCA-DNN model over datasets with different skewness levels are to be compared by observing their error values. The results suggest that CRT method can considerably improve both prediction accuracy and computational time of PCA-DNN model, while displaying a good generalization ability. Despite CRT method, SRT and LT methods resulted in high error values and overfitting issues, respectively.

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“…The activation function used for PCA-DNN model is softsign activation function, as stated in Eq. (15).…”

Section: = ∑ | | =1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

House price prediction: A data-centric aspect approach on performance of combined principal component analysis with deep neural network model

Mostofi¹,

Toğan²,

Başağa³

2021

JCEMI

View full text Add to dashboard Cite

show abstract

“…Principal Component Analysis (PCA) is a method of data dimensionality reduction. It derives a few principal components from the original variables and makes sure that they retain as much information of the original variables as possible [59][60][61]. This paper uses the PCA model to objectively weight the indicators of the six air pollutants in order to provide a basis for weighting in the evaluation model.…”

Section: Pca Modelmentioning

confidence: 99%

Analysis of the Effectiveness of Air Pollution Control Policies Based on Historical Evaluation and Deep Learning Forecast: A Case Study of Chengdu-Chongqing Region in China

et al. 2020

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Air pollution is a common problem for many countries around the world in the process of industrialization as well as a challenge to sustainable development. This paper has selected Chengdu-Chongqing region of China as the research object, which suffers from severe air pollution and has been actively involved in air pollution control in recent years to achieve sustainable development. Based on the historical data of 16 cities in this region from January 2015 to November 2019 on six major air pollutants, this paper has first conducted evaluation on the monthly air quality of these cities within the research period by using Principal Component Analysis and the Technique for Order Preference by Similarity to an Ideal Solution. Based on that, this paper has adopted the Long Short-Term Memory neural network model in deep learning to forecast the monthly air quality of various cities from December 2019 to November 2020. The aims of this paper are to enrich existing literature on air pollution control, and provide a novel scientific tool for design and formulation of air pollution control policies by innovatively integrating commonly used evaluation models and deep learning forecast methods. According to the research results, in terms of historical evaluation, the air quality of cities in the Chengdu-Chongqing region was generally moving in the same trend in the research period, with distinct characteristics of cyclicity and convergence. Year- on-year speaking, the effectiveness of air pollution control in various cities has shown a visible improvement trend. For example, Ya’an’s lowest air quality evaluation score has improved from 0.3494 in 2015 to 0.4504 in 2019; Zigong’s lowest air quality score has also risen from 0.4160 in 2015 to 0.6429 in 2019. Based on the above historical evaluation and deep learning forecast results, this paper has proposed relevant policy recommendations for air pollution control in the Chengdu-Chongqing region.

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“…Various neural network models are used in time series prediction, such as multi-layer perceptron [3], [4], recurrent neural networks [5], [6], long short-term memory [7], [8], gated recurrent unit [9], convolutional neural networks [10], [11] etc. Among these, neural attention models initially developed for neural machine translation have recently been used in time series prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of Experiments vs. TOPSIS to Select Hyperparameters of Neural Attention Models in Time Series Prediction

Midilli

Parshutin

2020

ITMS

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Attention models are used in neural machine translation to overcome the challenges of classical encoder-decoder models. In the present research, design of experiments and TOPSIS methods are used to select hyperparameters of a neural attention model for time series prediction. The configurations selected by both methods are compared with out-of-sample data in time interval between January 2020 and April 2020 when global economies were significantly impacted due to Covid-19 pandemic. Results demonstrated that both selection methods outperformed each other in terms of different output features. On the other hand, our results with more than 95 % coefficient of determination and less than 0.23 % MAPE verified that neural attention models had strong capabilities in exchange rate prediction even in extraordinary situations in global economies.

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Integration of Principal Component Analysis and Recurrent Neural Network to Forecast the Stock Price of Casablanca Stock Exchange

Cited by 75 publications

References 20 publications

House price prediction: A data-centric aspect approach on performance of combined principal component analysis with deep neural network model

House price prediction: A data-centric aspect approach on performance of combined principal component analysis with deep neural network model

Analysis of the Effectiveness of Air Pollution Control Policies Based on Historical Evaluation and Deep Learning Forecast: A Case Study of Chengdu-Chongqing Region in China

Design of Experiments vs. TOPSIS to Select Hyperparameters of Neural Attention Models in Time Series Prediction

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