A new financial data forecasting model using genetic algorithm and long short-term memory network

Huang, Yu‐Sheng; Gao, Yelin; Gan, Yan; Ye, Mao

doi:10.1016/j.neucom.2020.04.086

Cited by 84 publications

(22 citation statements)

References 37 publications

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“…Alhnaity & Abbod ( 2020 ) proposed a novel hybrid intelligent model for time series prediction using ANNs, support vector regression (SVR), feature extraction, with GAs to optimize weights. Prado et al ( 2020 ) proposed a novel ensemble methodology for forecasting aggregated long-term energy demand that included an ARIMA, ANN, fuzzy inference system model, adaptive neuro-fuzzy inference system, SVR, extreme ML, and GA. Huang et al ( 2021 ) presented a GA-based model for financial data forecasting using VMD and LSTM. Recently, Peng et al ( 2021 ) studied feature selection in the context of DNN models that use technical analysis indicators to predict stock price direction.…”

Section: Review Of the Literaturementioning

confidence: 99%

Integration of genetic algorithm with artificial neural network for stock market forecasting

Sharma

Hota

Brown

et al. 2021

Int J Syst Assur Eng Manag

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Traditional statistical as well as artificial intelligence techniques are widely used for stock market forecasting. Due to the nonlinearity in stock data, a model developed using the traditional or a single intelligent technique may not accurately forecast results. Therefore, there is a need to develop a hybridization of intelligent techniques for an effective predictive model. In this study, we propose an intelligent forecasting method based on a hybrid of an Artificial Neural Network (ANN) and a Genetic Algorithm (GA) and uses two US stock market indices, DOW30 and NASDAQ100, for forecasting. The data were partitioned into training, testing, and validation datasets. The model validation was done on the stock data of the COVID-19 period. The experimental findings obtained using the DOW30 and NASDAQ100 reveal that the accuracy of the GA and ANN hybrid model for the DOW30 and NASDAQ100 is greater than that of the single ANN (BPANN) technique, both in the short and long term.

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Section: Review Of the Literaturementioning

confidence: 99%

Integration of genetic algorithm with artificial neural network for stock market forecasting

Sharma

Hota

Brown

et al. 2021

Int J Syst Assur Eng Manag

View full text Add to dashboard Cite

show abstract

“…In the last decade, various machine learning and deep learning tech-niques have also gained attention in solving problems of stock forecasting. Two structured neural network models, namely recurrent neural network (RNN) [16] and long short-term memory (LSTM) [17,18], are now considered as state-ofthe-art in the resolution of stock forecasting problems, due to their inherent ability for processing varying length sequences and predicting future trends.…”

Section: Introductionmentioning

confidence: 99%

Recurrent dictionary learning for state-space models with an application in stock forecasting

et al. 2021

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In this work, we introduce a new modeling and inferential tool for dynamical processing of time series. The approach is called recurrent dictionary learning (RDL). The proposed model reads as a linear Gaussian Markovian state-space model involving two linear operators, the state evolution and the observation matrices, that we assumed to be unknown. These two unknown operators (that can be seen interpreted as dictionaries) and the sequence of hidden states are jointly learnt via an expectation-maximization algorithm. The RDL model gathers several advantages, namely online processing, probabilistic inference, and a high model expressiveness which is usually typical of neural networks. RDL is particularly well suited for stock forecasting. Its performance is illustrated on two problems: next day forecasting (regression problem) and next day trading (classification problem), given past stock market observations. Experimental results show that our proposed method excels over state-of-the-art stock analysis models such as CNN-TA, MFNN, and LSTM.

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“…In regard to the domain of signal extraction and forecasting, GA has certainly played an active role in the recent decades. Some of the selected topics include: bankruptcy prediction [13][14][15], credit scoring [16,17], crude oil price [18][19][20], tourism demand [21][22][23], the beta systematic risk [24], financial data [25,26], gas demand [27], electric load [28,29], wind speed [30], rainfall [31], etc. Via comprehensively exploring existing literature, it came to our attention that although GA has been applied jointly with many data analytics techniques in practice, to name a few, neural network, principal component analysis, wavelet analysis, long and short memory network, support vector machines.…”

Section: Introductionmentioning

confidence: 99%

Fusing Nature with Computational Science for Optimal Signal Extraction

Hassani

Yeganegi

Huang

2021

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Fusing nature with computational science has been proved paramount importance and researchers have also shown growing enthusiasm on inventing and developing nature inspired algorithms for solving complex problems across subjects. Inevitably, these advancements have rapidly promoted the development of data science, where nature inspired algorithms are changing the traditional way of data processing. This paper proposes the hybrid approach, namely SSA-GA, which incorporates the optimization merits of genetic algorithm (GA) for the advancements of Singular Spectrum Analysis (SSA). This approach further boosts the performance of SSA forecasting via better and more efficient grouping. Given the performances of SSA-GA on 100 real time series data across various subjects, this newly proposed SSA-GA approach is proved to be computationally efficient and robust with improved forecasting performance.

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A new financial data forecasting model using genetic algorithm and long short-term memory network

Cited by 84 publications

References 37 publications

Integration of genetic algorithm with artificial neural network for stock market forecasting

Integration of genetic algorithm with artificial neural network for stock market forecasting

Recurrent dictionary learning for state-space models with an application in stock forecasting

Fusing Nature with Computational Science for Optimal Signal Extraction

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