Research on the Forecast of Stock Price Index Based on BiLSTM-GRU

Wang, Yuanyuan

doi:10.1109/fcsit57414.2022.00027

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…By combining information from both directions, the BiLSTM effectively overcomes the limitations of one-way LSTMs, making it particularly suitable for tasks involving natural language processing, speech recognition, and various sequence modeling challenges. The BiLSTM expression show in (7) [26].…”

Section: Bilstmmentioning

confidence: 99%

Unveiling deep learning powers: LSTM, BiLSTM, GRU, BiGRU, RNN comparison

Shaikh,

Ramadass

2024

IJEECS

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Deep learning algorithms have revolutionized various fields by achieving remarkable results in time series analysis. Among the different architectures, recurrent neural networks (RNNs) have played a significant role in sequential data processing. This study presents a comprehensive comparison of prominent RNN variants: long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), bidirectional GRU (BiGRU), and RNN, to analyze their respective strengths and weaknesses of national stock exchange India (NSEI). The Python application developed for this research aims to evaluate and determine the most effective algorithm among the variants. To conduct the evaluation, data from the public domain covering the period from 1/1/2004 to 30/06/2023 is collected. The dataset considers significant events such as demonetization, market crashes, the COVID-19 pandemic, downturns in the automobile sector, and rises in unemployment. Stocks from various sectors including banking, automobile, oil and gas, metal, and Pharma are selected for analysis. Finally, the results reveal that algorithm performance varies across different stocks. Specifically, in certain cases, BiLSTM outperforms, while in others, both BiGRU and LSTM are surpassed. Notably, the overall performance of simple RNN is consistently the lowest across all stocks.

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Section: Bilstmmentioning

confidence: 99%

Unveiling deep learning powers: LSTM, BiLSTM, GRU, BiGRU, RNN comparison

Shaikh,

Ramadass

2024

IJEECS

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“…Prior research found LSTMs captured price trends and changes much more accurately over traditional methods like ARIMA [18]. Bidirectional LSTM models with additional gated recurrent units have also been proposed for stock forecasting with significantly minimized deviations between predictions and ground truth [19].…”

Section: Introductionmentioning

confidence: 99%

The PSR-Transformer Nexus: A Deep Dive into Stock Time Series Forecasting

Phien,

Platos

2023

IJACSA

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Accurate stock market forecasting has remained an elusive endeavor due to the inherent complexity of financial systems dynamics. While deep neural networks have shown initial promise, robustness concerns around long-term dependencies persist. This research pioneers a synergistic fusion of nonlinear time series analysis and algorithmic advances in representation learning to enhance predictive modeling. Phase space reconstruction provides a principled way to reconstruct multidimensional phase spaces from single variable measurements, elucidating dynamical evolution. Transformer networks with self-attention have recently propelled state-of-the-art results in sequence modeling tasks. This paper introduces PSR-Transformer Networks specifically tailored for stock forecasting by feeding PSR interpreted constructs to transformer encoders. Extensive empirical evaluation on 20 years of historical equities data demonstrates significant accuracy improvements along with enhanced robustness against LSTM, CNN-LSTM and Transformer models. The proposed interdisciplinary fusion establishes new performance benchmarks on modeling financial time series, validating synergies between domain-specific reconstruction and cutting-edge deep learning.

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