Research on stock trend prediction method based on optimized random forest

Yin, Lili; Li, Benling; Zhang, Rubo

doi:10.1049/cit2.12067

Cited by 26 publications

(10 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It will be significantly subjective according to the nature of the data and other case-by-case criteria to determine which model is better. 38,39 The final XGBoost model is then tested for its generalization capability using the test data set (or deployment data set). It is observed from the confusion matrices in Figure 17a that the performance of the model in predicting the modes are consistent for all the different data sets especially for the main classes C-1, C-2, and C-3.…”

Section: Model Performance Comparison Analysismentioning

confidence: 99%

Investigative Analysis of Automatic Mode Detection for a Lubricant Base Oil Production Plant Using PCA and Machine-Learning Models

Mohd fadzil,

Razali,

Zabiri

et al. 2024

ACS Omega

View full text Add to dashboard Cite

Lubricants are important fluids and are commonly used to suppress friction between two metallic surfaces and as a medium for heat transportation. In an industrial plant considered in this study, the base oil mode changes can only be detected based on the kinematic viscosity values obtained using lab analysis. Since the lab analysis data are only available every 8 h, detecting the change in the production modes for 4, 6, and 10 cSt and the transitions among them are significantly delayed, causing unnecessary off-spec products that have to be directed to the slopping tank. In this paper, the innovativeness of the work comes from the idea of trying to unravel the underlying pattern of the plant data that correlate to the changes in the base oil modes and using that to classify hourly the kinematic viscosity values. Hence, a novel industrial application is presented to predict the class of base oil mode change on an hourly basis that can significantly reduce the losses in terms of off spec products and sloping tank wastes. The modes are segregated into three classes based on the values of kinematic viscosity. The classes are C-1 (4 cSt), C-2 (6 cSt), and C-3 (10 cSt). Anything in between the stipulated thresholds is called transition [T-12 (C-1 to C-2), T-21(C-2 to C-1), T-23 (C-2 to C-3), T-31 (C-3 to C-1), and T-32 (C-3 to C-2)]. To unravel the pattern, principal component analysis (PCA) is utilized on 42,000 operating plant data. After a thorough analysis, the third principal component provides the highest correlation to the eight classes of the base oil mode changes [C-1 (4 cSt), C-2 (6 cSt), and C-3 (10 cSt) and the transitions T-12 (C-1 to C-2), T-21(C-2 to C-1), T-23 (C-2 to C-3), T-31 (C-3 to C-1), and T-32 (C-3 to C-2)]. This third principal component is then utilized together with plant process variable values as inputs to four machine learning models, namely, XGBOOST, Random Forest, and CatBoost algorithms to predict the mode of the base oil hourly. The overall comparison analysis shows that utilizing the XGBoost algorithm for the prediction of the eight classes of the base oil modes at a faster hourly rate results in the most consistent classification accuracy of 92.96% for the test set and 89.22% in the deployment set. This capability to predict the mode change in the hourly basis can significantly reduce the losses in terms of off spec products in the production line.

show abstract

Section: Model Performance Comparison Analysismentioning

confidence: 99%

Investigative Analysis of Automatic Mode Detection for a Lubricant Base Oil Production Plant Using PCA and Machine-Learning Models

Mohd fadzil,

Razali,

Zabiri

et al. 2024

ACS Omega

View full text Add to dashboard Cite

show abstract

“…The results were more accurate when using the RF. Yin et al (2021) used decision trees and RF. Despite seeming redundant to use both together, the approach of the authors consisted of using the decision trees to select features that better represent the sample for further establishing the predictions using the RF.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Investments in Times of Uncertainty: Formation of Portfolios Using Random Forest

Silva¹,

Montebello

Silva

2023

regea

View full text Add to dashboard Cite

Objective: based on a systematic approach using machine learning, this research aims to propose a model of selection and allocation of assets that allows for building profitable and safe portfolios, even in times of insecurity and low predictability.Methodology: we used the machine learning algorithm called random forest to associate the independent variables with a dependent one and learn the probability of positive returns in the month following the data collection. According to the probabilities, the stocks were allocated into long, short, or non-allocated portfolios. Finally, we allocated a share of gold, which is a protection asset much used in times of crisis and uncertainty.Results and contributions: the study reached its goal and demonstrated being possible to build profitable and safe investment portfolios, even in times of greater uncertainty and volatility, as in 2020 due to the Covid-19 pandemic. We found that the model is effective in moments of crisis and also of greater predictability, as in the period from 2016 to 2019 when the stock exchange has an uptrend.Relevance: the relevance of this study points to an unprecedented historical context in Brazil, where uncertainties regarding both the local and world economy have demanded advanced studies of prediction to minimize risks and contribute to results for investors. In addition, we highlight that following a short period of low Selic (2019 to 2021), the Central Bank increased the rate again, raising the interest more in profitable and safer assets than the investment in stocks.

show abstract

“…In [25], optimized custom moving average most suitable for stock time series smoothing was proposed to smooth stock price series and forecast trend direction. In [26], the authors used the exponential smoothing method to process the initial data, calculate the relevant technical indicators as the characteristics to be selected, and optimize the random forest to predict the stock trend. In [27], the authors proposed a hybrid extreme gradient boosting framework and auto regressive integrated moving average model to predict stock price.…”

Section: Related Workmentioning

confidence: 99%

Research on HMM-Based Efficient Stock Price Prediction

2022

Mobile Information Systems

View full text Add to dashboard Cite

Stock market is one of the most important parts of the investment market. Compared with other industries, the stock market not only has a higher rate of return on investment but also has a higher risk, and stock price prediction has always been a close concern of investors. Therefore, the research on stock price prediction methods and how to reduce the error of stock price prediction has become a hot topic for many scholars at home and abroad. In recent years, the development of computer technology such as machine learning and econometric method makes the stock price prediction more reliable. Due to the hidden Markov nature of stock price, this paper proposes a stock price prediction method based on hidden Markov model (HMM). To be specific, since the data of stock price have continuity in time series, it is necessary to extend the discrete HMM to the continuous HMM, and then put forward the up and down trend prediction model based on the continuous HMM. The first-order continuous HMM is extended to the second-order continuous HMM, and the stock price is predicted by combining the prediction method of fluctuation range. As a result, the proposed second-order continuous HMM-based stock price prediction model is simulated on Hang Seng Index (HSI), one of the earliest stock market indexes in Hong Kong. The evaluation results on six months HSI show that the predicted value of the proposed model is very close to the actual value and outperforms three benchmarks in terms of RMSE, MAE, and R2.

show abstract

Research on stock trend prediction method based on optimized random forest

Cited by 26 publications

References 29 publications

Investigative Analysis of Automatic Mode Detection for a Lubricant Base Oil Production Plant Using PCA and Machine-Learning Models

Investigative Analysis of Automatic Mode Detection for a Lubricant Base Oil Production Plant Using PCA and Machine-Learning Models

Investments in Times of Uncertainty: Formation of Portfolios Using Random Forest

Research on HMM-Based Efficient Stock Price Prediction

Contact Info

Product

Resources

About