An efficient real-time stock prediction exploiting incremental learning and deep learning

Singh, Tinku; Kalra, Riya Kaur; Mishra, Suryanshi; Satakshi,; Kumar, Manish

doi:10.1007/s12530-022-09481-x

Cited by 20 publications

(6 citation statements)

References 31 publications

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“…The F-score metric is employed for performance evaluation, validated through a replication study. The other real-time forecasting and ML studies [17][18][19] show the importance and efficiency of ML methods in this domain.…”

Section: Related Workmentioning

confidence: 99%

Water Quality Monitoring on Streaming Data

Kumar,

Singh,

Kumar

et al. 2024

CJACSIT

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The increasing contamination of natural water bodies due to diverse human activities necessitates a comprehensive approach to monitoring water quality, especially considering its widespread use in daily life. This study addresses the escalating contamination of natural water bodies, emphasizing the need for a robust real-time water quality monitoring system. Focused on evaluating Triveni Sangam, Prayagraj, where the Ganga and Yamuna rivers converge, the study recognizes the crucial role of continuous monitoring in safeguarding precious water resources. To achieve this, a sophisticated framework has been proposed, leveraging a Spark server to simulate streaming data. This dynamic approach ensures uninterrupted and real-time assessment of water quality, crucial for the effective management of water resources. The system categorizes training data using the Water Quality Index (WQI) and employs Naive Bayes classification for real-time data, achieving an impressive accuracy of 82.21%. The results underscore the effectiveness of learning from streaming data, emphasizing its utility for monitoring water quality in real-time. This study contributes significantly to ongoing water resource management initiatives but also highlights the pivotal role of machine learning in addressing pressing environmental challenges.

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Section: Related Workmentioning

confidence: 99%

Water Quality Monitoring on Streaming Data

Kumar,

Singh,

Kumar

et al. 2024

CJACSIT

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“…The objective of prediction research has been largely beyond the capability of traditional Arti cial Intelligence (AI) research which, has mainly focused on developing intelligent systems that are supposed to emulate human intelligence [4] [5].…”

Section: Motivationmentioning

confidence: 99%

“…Data of eight different IT industries was accumulated from the Yahoo nance Website, beginning from January 2012 till the end including December 2022. 4. Results were validated and compared using RMSE, MAPE, MSE evaluation parameters.…”

Section: Motivationmentioning

confidence: 99%

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Stock Closing Price Prediction of Indian Tech Companies using Deep Neural Networks

Divyansh,

Moonis,

Raghav

et al. 2023

Preprint

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In today’s economic world forecast and study of stock market data have got a vital role but, stock price forecasting has been hampered by its nonlinearity and high volatility of financial time series, to maximize the profits and reduce the risk involved the investors are eager to find different ways to forecast upcoming trends. It’s a challenging task to crack stock portfolio allocation in such a suitable way to invest in selected stocks that efficiently minimize the risk involved while capitalizing on profit. The effects of rival stocks in the stock market and the impact of mutation points on the precision of target stock predictions are rarely considered as market sentiment by the current prediction models. To validate which deep learning algorithm is best suited for stock market prediction analysis, we have espoused four deep learning algorithms namely Long Short-Term Memory (LSTM), Autoregressive Integrated Memory (ARIMA), Bi-directional Long Short-Term Memory (Bi-LSTM), and Gate Recurrent Units (GRU). On the basis of these mentioned evaluation parameters i.e., Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE) we have done the accuracy tests of the listed deep learning algorithms with evaluative stock prediction graph. Results have shown Bi-LSTM to perform exceptionally well for time series analysis. Regarding LSTM and GRU, the results have more or less stayed the same. While ARIMA gave an un-stable performance thus rendering its usefulness for our analysis.

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“…Early forecasting methods mainly include fundamental analysis and technical analysis. Fundamental analysis focuses on variables representing a company's financial condition, industry position, and market outlook to assess the investment value of a stock [2]. Technical analysis, on the other hand, is based on past stock price data and uses charts and indicators to predict future price movements.…”

Section: Introductionmentioning

confidence: 99%

Comparison of stock price prediction models for linear models, random forest and LSTM

2024

ACE

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With the rapid development of financial markets, accurate stock price prediction is significant to investors and financial institutions. Many researchers proposed stock price prediction models, including linear models, random forests, and LSTMs. However, few studies have comprehensively compared the three models. This study aims to fill this gap by analysing the forecasting effectiveness of different models through empirical studies. This research is to explore the application of linear models, random forests, and LSTM models in predicting stock prices and analyse and compare the principles, advantages and disadvantages, and the scope of application of these three models. According to the analysis, they all have their scope of application and limitations in different situations. In practical application, the appropriate model can be chosen for prediction and analysis according to the specific data sets and research purpose. Meanwhile, it is also possible to try to integrate and improve different models to get better prediction results. In addition, the influence of data quality and completeness, feature selection and extraction from the prediction results should be noted to improve the prediction accuracy and stability of the model. In conclusion, this thesis provides some references and lessons for related studies and practical applications by analysing and comparing the applications of LSTM, linear models, and random forests in predicting stock prices.

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An efficient real-time stock prediction exploiting incremental learning and deep learning

Cited by 20 publications

References 31 publications

Water Quality Monitoring on Streaming Data

Water Quality Monitoring on Streaming Data

Stock Closing Price Prediction of Indian Tech Companies using Deep Neural Networks

Comparison of stock price prediction models for linear models, random forest and LSTM

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