Indonesian Stock Price Prediction including Covid19 Era Using Decision Tree Regression

Hindrayani, Kartika Maulida; Fahrudin, Tresna Maulana; Aji, Roby; Safitri, Eristya Maya

doi:10.1109/isriti51436.2020.9315484

Cited by 23 publications

(8 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, the accuracy of the white-box model is limited due to the harsh operating environment and the complex model structure [ 28 ]. Hindrayani et al observed the data stocks of telecommunication companies and generalized the inference for each prediction decision for each company stock by constructing a decision tree model, and the final results obtained the smallest mean absolute percentage error [ 29 ]. Shakeri et al proposed an expert system based on fuzzy rules.…”

Section: Literature Reviewmentioning

confidence: 99%

An interpretable model for stock price movement prediction based on the hierarchical belief rule base

Yin,

Zhang,

et al. 2023

Heliyon

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

An interpretable model for stock price movement prediction based on the hierarchical belief rule base

Yin,

Zhang,

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…DT-R is a regression method in which model development is conducted using a tree structure that has root, branches, and leaf nodes [25]. The decision tree is completed by selecting the appropriate attribute with attribute selection measures (ASM).…”

Section: Machine Learning Testingmentioning

confidence: 99%

Data Augmentation Technique Using Two Step SMOTE for Electronic-nose Signal in Breath Ketone Level Detection

2023

IJIES

View full text Add to dashboard Cite

Breath acetone concentrations were found to be correlated with blood ketone levels. Based on this evidence, predicting blood ketone levels using breath analysis and machine learning (ML) becomes possible. Nevertheless, a good ML model requires a large amount of training data. Under certain conditions, it is difficult to collect large amounts of data such as during the Covid-19 pandemic. To overcome this problem, we propose an augmentation technique to extend the number of training datasets using two step synthetic minority oversampling (SMOTE). The first step was to increase the amount of training data by combining it with synthetic data, while the second step was to balance the data at each ketone level. The strategy for using SMOTE with regression was further explained since this study aims to predict ketone levels with numerical output values and SMOTE is typically used in classification cases. The proposed method was evaluated by entering the data into several ML methods such as deep neural network regression (DNN-R), linear regression (ML-R), ransac regression (RC-R), K-nearest neighbour regression (KNN-R), decision tree regression (DT-R), random forest regression (RF-R), Ada boost regression (AD-R), Gradient boost regression (GB-R) and XG-boost regression (XGB-R). Based on the test results, when compared without the proposed method, an increase in accuracy was obtained on

show abstract

“…The result differentiates between regression trees and categorization trees, although the classification trees are similar to regression trees constructed from Root, Node, and Leaf. A regression tree's output is a continuous or real value rather than a class [11].…”

Section: Decision Treementioning

confidence: 99%

Stock Price Prediction Based on Machine Learning

Liu¹,

Peng²,

Yu³

2022

Advances in Economics, Business and Management Research

View full text Add to dashboard Cite

The stock market is riddled with uncertainty and risks, taking one fallacious decision could lead to huge loss. Therefore, stock market prediction is of great interest to many stock investors. The paper adopts four machine learning models including Decision Tree Regression, Linear Regression, Random Forest Regression Support Vector Regression, respectively, to make prediction on the price of Apple Inc. During the experiment, data in the recent three years were used to train the models in order to make prediction. Moreover, by calculating the mean squared error, the comparison between different models were made. The obtained results showed that the Support Vector Linear Regression model shows a better performance than other models, which is instrumental to the related stock investors in financial markets.

show abstract

Indonesian Stock Price Prediction including Covid19 Era Using Decision Tree Regression

Cited by 23 publications

References 17 publications

An interpretable model for stock price movement prediction based on the hierarchical belief rule base

An interpretable model for stock price movement prediction based on the hierarchical belief rule base

Data Augmentation Technique Using Two Step SMOTE for Electronic-nose Signal in Breath Ketone Level Detection

Stock Price Prediction Based on Machine Learning

Contact Info

Product

Resources

About