Financial High-Frequency Time Series Forecasting Based on Sub-Step Grid Search Long Short-Term Memory Network

Si, Luo; Tian, Cong

doi:10.1109/access.2020.3037102

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…Grid search is an exhaustive search approach employed for hyperparameter tuning, ensuring optimal model performance. By systematically working through multiple combinations of hyperparameter sets, it evaluates which combination gives the best performance based on a scoring technique [15,16].…”

Section: Grid Search Optimizationmentioning

confidence: 99%

Sentiment-Driven Forecasting LSTM Neural Networks for Stock Prediction-Case of China Bank Sector

Jin

2023

IJACSA

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This study explores the predictive analysis of public sentiment in China's financial market, focusing on the banking sector, through the application of machine learning techniques. Specifically, it utilizes the Baidu Index and Long Short-Term Memory (LSTM) networks. The Baidu Index, akin to China's version of Google Trends, serves as a sentiment barometer, while LSTM networks excel in analyzing sequential data, making them apt for stock price forecasting. Our model integrates sentiment indices from Baidu with historical stock data of significant Chinese banks, aiming to unveil how digital sentiment influences stock price movements. The model's forecasting prowess is rigorously evaluated using metrics such as R-squared (R 2 ), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and confusion matrices, the latter being instrumental in assessing the model's capability in correctly predicting stock up or down movements. Our findings predominantly showcase superior prediction performance of the sentiment-based LSTM model compared to a standard LSTM model. However, effectiveness varies across different banks, indicating that sentiment integration enhances prediction capabilities, yet individual stock characteristics significantly contribute to the prediction accuracy. This inquiry not only underscores the importance of integrating public sentiment in financial forecasting models but also provides a pioneering framework for leveraging digital sentiment in financial markets. Through this endeavor, we offer a robust analytical tool for investors, policymakers, and financial institutions, aiding in better navigation through the intricate financial market dynamics, thereby potentially leading to more informed decision-making in the digital age.

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Section: Grid Search Optimizationmentioning

confidence: 99%

Sentiment-Driven Forecasting LSTM Neural Networks for Stock Prediction-Case of China Bank Sector

Jin

2023

IJACSA

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“…Queen GA-SVR and multivariate adaptive regression splines are implemented for financial prediction by authors in [52] and performance comparisons is done with existing methods. Sub-step grid search is used for parameter optimization of long-short term memory applied for high-frequency financial time series and achieved higher efficiency [53]. Issues, ways of hybridization, complexity analysis, and feasible industrial applications of nature inspired intelligent optimization algorithms are highlighted in [54].…”

Section: Related Studiesmentioning

confidence: 99%

Intelligent Financial Forecasting With an Improved Chemical Reaction Optimization Algorithm Based Dendritic Neuron Model

2022

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Chemical reaction optimization (CRO) algorithm is a robust metaheuristic by simulating the process of natural chemical reaction and has been applied for solving numerous realistic problems. However, it is suffering from low population diversity, slow convergence, and deprived from local search ability. To boost the search operation of CRO, this article proposes an improved CRO method termed as ICRO. We achieve the improvements in two phases. First, to start the search operation with a better-quality population, we propose a new method to initialize population which helps in enhancing diversification of the search space. Second, to intensify the local search ability we integrate Nelder-Mead simplex method with CRO. The ICRO is then evaluated on a few benchmark functions for optimization and found apposite. Dendritic neuron model (DNM) using additive and multiplicative-based aggregation functions has been emerging as a machine learning approach and found successful in many engineering applications. This study attempts to advance the predictive accuracy of DNM through maintaining a decent steadiness between exploitation and exploration of its search space with the proposed ICRO, termed as ICRO-DNM. The powerful global search ability of ICRO synergies with better approximation capability of DNM thus, able to overcome the limitations of conventional back propagation learning based DNM. ICRO-DNM is evaluated on closing price prediction for four stock index and net asset values of four mutual fund datasets and found efficient. The consequences of this attempt are: (1) Proposed ICRO shows robust parameter optimization ability for benchmark functions and DNM compared to basic CRO, particle swarm optimization (PSO), and genetic algorithm (GA); (2) The learning paradigm formed due to reasonable amalgamation of ICRO and DNM is pretty able to capture the underlying uncertainties coupled with financial data, produces more precise and steady predictions and significantly different from other forecasts.

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“…But there are also works focused the extraction of the information contained in the high-frequency component. These works are based on a variety of statistical methods and learning models; see, e.g., Brooks and Hinich (2006), Christensen et al (2012), Granger (1998), Li et al (2020), Luo and Tian (2020), and references therein.…”

Section: Introductionmentioning

confidence: 99%

Predictors for high frequency processes based on rational polynomials approximation of periodic exponentials

Dokuchaev¹

2022

Preprint

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Financial High-Frequency Time Series Forecasting Based on Sub-Step Grid Search Long Short-Term Memory Network

Cited by 11 publications

References 18 publications

Sentiment-Driven Forecasting LSTM Neural Networks for Stock Prediction-Case of China Bank Sector

Sentiment-Driven Forecasting LSTM Neural Networks for Stock Prediction-Case of China Bank Sector

Intelligent Financial Forecasting With an Improved Chemical Reaction Optimization Algorithm Based Dendritic Neuron Model

Predictors for high frequency processes based on rational polynomials approximation of periodic exponentials

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