Comparative Analysis of K-NN and Naïve Bayes Methods to Predict Stock Prices

Abstract— Buying and selling shares is a transaction that is widely carried out at this time, especially buying and selling stocks online which are widely available in the market, to make buying and selling shares require ability or knowledge so that the buying and selling of shares are profitable, to be able to help economic players predict prices. Profit shares or not purchased in the future, this research will conduct stock price predictions using classification methods, namely K-Nearest Neighbor and Naïve Bayes, to predict the stock price data used for one month in minute levels totalling 39065 data, based on prediction results. The highest results obtained were using Naïve Bayes with an accuracy value of 69.38 then the K-Nearest Neighbor method with a K = 5 value of 67.25%, based on these results it can be concluded that the use of the K-Nearest Neighbor and Naïve Bayes methods for prediction share price not yet owned I high accuracy, so it can be combined with other methods or by using other variable predictors.

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COMPARING NAIVE BAYES, K-NEAREST NEIGHBOR, AND NEURAL NETWORK CLASSIFICATION METHODS OF SEAT LOAD FACTOR IN LOMBOK OUTBOUND FLIGHTS

Jurnal Matematika Statistika dan Komputasi ◽

10.20956/jmsk.v16i2.7864 ◽

2019 ◽

Vol 16 (2) ◽

pp. 187

Author(s):

Mega Luna Suliztia ◽

Achmad Fauzan

Keyword(s):

Neural Network ◽

Nearest Neighbor ◽

Naive Bayes ◽

Naïve Bayes ◽

Load Factor ◽

Classification Methods ◽

Affecting Factors ◽

K Nearest Neighbor ◽

Accuracy Rate ◽

Ticket Prices

Classification is the process of grouping data based on observed variables to predict new data whose class is unknown. There are some classification methods, such as Naïve Bayes, K-Nearest Neighbor and Neural Network. Naïve Bayes classifies based on the probability value of the existing properties. K-Nearest Neighbor classifies based on the character of its nearest neighbor, where the number of neighbors=k, while Neural Network classifies based on human neural networks. This study will compare three classification methods for Seat Load Factor, which is the percentage of aircraft load, and also a measure in determining the profit of airline.. Affecting factors are the number of passengers, ticket prices, flight routes, and flight times. Based on the analysis with 47 data, it is known that the system of Naïve Bayes method has misclassifies in 14 data, so the accuracy rate is 70%. The system of K-Nearest Neighbor method with k=5 has misclassifies in 5 data, so the accuracy rate is 89%, and the Neural Network system has misclassifies in 10 data with accuracy rate 78%. The method with highest accuracy rate is the best method that will be used, which in this case is K-Nearest Neighbor method with success of classification system is 42 data, including 14 low, 10 medium, and 18 high value. Based on the best method, predictions can be made using new data, for example the new data consists of Bali flight routes (2), flight times in afternoon (2), estimate of passenger numbers is 140 people, and ticket prices is Rp.700,000. By using the K-Nearest Neighbor method, Seat Load Factor prediction is high or at intervals of 80% -100%.

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PERBANDINGAN METODE K-NN DAN BAYES PADA MISSING IMPUTATION

Jurnal Teknologi Informasi dan Terapan ◽

10.25047/jtit.v5i2.84 ◽

2019 ◽

Vol 5 (2) ◽

pp. 85-90

Author(s):

Taufiq Rizaldi ◽

Fendik Eko Purnomo ◽

Aji Seto Arifianto

Keyword(s):

Data Collection ◽

Nearest Neighbor ◽

Naive Bayes ◽

Naïve Bayes ◽

Data Loss ◽

Optimal Outcome ◽

K Nearest Neighbor ◽

Bayes Methods ◽

Data Collection Process ◽

The Mean

The problem of data loss in a dataset is experienced in surveys for data collection which are usually caused by no response from units or items during the survey data collection process. The loss of a data can significantly influence the results of a study. The inaccuracy in choosing a solution to overcome these problems can result in a less than optimal outcome that tends to be biased. Some methods that are widely used to solve these problems are using the K Nearest Neighbor (K-NN) and Naïve Bayes methods, the main purpose of this study is to compare the performance of the two methods. From the results of the K-NN, the results were better, where the Mean Square Error (MSE) is bigger than 1 and MAPE around 10-16%, while Naïve Bayes got MSE values bigger than 1 and MAPE around 26%.

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Perbandingan Kinerja Metode Naive Bayes dan K-Nearest Neighbor untuk Klasifikasi Artikel Berbahasa indonesia

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.201854773 ◽

2018 ◽

Vol 5 (4) ◽

pp. 427 ◽

Cited By ~ 1

Author(s):

Riri Nada Devita ◽

Heru Wahyu Herwanto ◽

Aji Prasetya Wibawa

Keyword(s):

Nearest Neighbor ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Classification Methods ◽

K Nearest Neighbor ◽

Bayes Method ◽

K Nearest Neighbors ◽

Naive Bayes Method ◽

Main Factor

Kecocokan isi artikel dengan sebuah tema jurnal menjadi faktor utama diterima tidaknya sebuah artikel. Tetapi masih banyak mahasiswa yang bingung untuk menentukan jurnal yang sesuai dengan artikel yang dimilikinya. Untuk itu diperlukannya sebuah metode klasifikasi dokumen yang dapat mengelompokkan artikel secara otomatis dan akurat. Terdapat banyak metode klasifikasi yang dapat digunakan. Metode yang digunakan dalam penelitian ini adalah Naive Bayes dan sebagai baseline digunakan metode K-Nearest Neighbor. Metode Naive Bayes dipilih karena dapat menghasilkan akurasi yang maksimal dengan data latih yang sedikit. Sedangkan metode K-Nearest Neighbor dipilih karena metode tersebut tangguh terhadap data noise. Kinerja dari kedua metode tersebut akan dibandingkan, sehingga dapat diketahui metode mana yang lebih baik dalam melakukan klasifikasi dokumen. Hasil yang didapatkan menunjukkan metode Naive Bayes memiliki kinerja yang lebih baik dengan tingkat akurasi 70%, sedangkan metode K-Nearest Neighbor memiliki tingkat akurasi yang cukup rendah yaitu 40%. AbstractOne way to be accepted in a journal conference and get the publication is to create an article with perfect suitability content of the journal. Matching the content of the article with a journal theme is the main factor for acceptability an article. But there are still many students who are confused to choose the journal in accordance with the articles it has. So we need a method to classification article documents category automatically and accurately group articles. There are many classification methods that can be used. The method used in this study is Naive Bayes and as a baseline the K-Nearest Neighbor method. Naive Bayes method is chosen because it can produce maximum accuracy with little training data. While K-Nearest Neighbor method was chosen because the method is robust to data noise. The performance of the two methods will be compared, so we can be known which method is better in classifying the document. The results show that the Naive Bayes method performs is more accurate with 70% accuracy and K-Nearest Neighbors method has a fairly low accuracy of 40% on classification test.

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Classification methods comparison for customer churn prediction in the telecommunication industry

International Journal of ADVANCED AND APPLIED SCIENCES ◽

10.21833/ijaas.2021.12.001 ◽

2021 ◽

Vol 8 (12) ◽

pp. 1-8

Author(s):

Makruf et al. ◽

Keyword(s):

Decision Tree ◽

Nearest Neighbor ◽

Naive Bayes ◽

Naïve Bayes ◽

Telecommunication Service ◽

Support Vector ◽

Telecommunication Industry ◽

Classification Methods ◽

K Nearest Neighbor ◽

Customer Churn

The need for telecommunication services has increased dramatically in schools, offices, entertainment, and other areas. On the other hand, the competition between telecommunication companies is getting tougher. Customer churn is one of the areas that each company gains more competitive advantage. This paper proposes a comparison of several classification methods to make a prediction whether the customers cancel the subscription to a telecommunication service by highlighting key factors of customer churn or not. The comparison is non-trivial due to the urgent requirements from the telecommunication industry to infer the most appropriate techniques in analyzing their customer churn. This comparison is often of huge commercial value. The result shows that Artificial Neural Network (ANN) can predict churn with an accuracy of 79%, Support Vector Machine (SVM) with 78% accuracy, Gaussian Naïve Bayes, and K-Nearest Neighbor (KNN) with 75% accuracy, while Decision Tree with 70% accuracy. Moreover, the technique with the highest F-Measure is Gaussian Naïve Bayes with 65% and the technique with the lowest one is Decision Tree with 49%. Hence, ANN and Gaussian Naïve Bayes are two methods with high recommendation to predict the customer churn in the telecommunication industry.

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Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain

Earth Science Informatics ◽

10.1007/s12145-021-00653-y ◽

2021 ◽

Author(s):

Sherif Ahmed Abu El-Magd ◽

Sk Ajim Ali ◽

Quoc Bao Pham

Keyword(s):

Random Forest ◽

Spatial Modeling ◽

Nearest Neighbor ◽

Naive Bayes ◽

Naïve Bayes ◽

K Nearest Neighbor

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Combination of Naïve Bayes Classifier and K-Nearest Neighbor (cNK) in the Classification Based Predictive Models

Computer and Information Science ◽

10.5539/cis.v6n3p48 ◽

2013 ◽

Vol 6 (3) ◽

Cited By ~ 5

Author(s):

Elma Zannatul Ferdousy ◽

Md. Mafijul Islam ◽

M. Abdul Matin

Keyword(s):

Predictive Models ◽

Nearest Neighbor ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

K Nearest Neighbor ◽

Naïve Bayes Classifier

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Performance of Naïve Bayes, C4.5 and KNN using Breast Cancer, Iris and Hypothyroid Datasets

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c8795.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 2193-2197

Keyword(s):

Breast Cancer ◽

Data Mining ◽

Nearest Neighbor ◽

Naive Bayes ◽

Naïve Bayes ◽

Specific Pattern ◽

K Nearest Neighbor ◽

Data Mining Technique ◽

Digital Format ◽

Tree Classifier

Data mining usually specifies the discovery of specific pattern or analysis of data from a large dataset. Classification is one of an efficient data mining technique, in which class the data are classified are already predefined using the existing datasets. The classification of medical records in terms of its symptoms using computerized method and storing the predicted information in the digital format is of great importance in the diagnosis of various diseases in the medical field. In this paper, finding the algorithm with highest accuracy range is concentrated so that a cost-effective algorithm can be found. Here the data mining classification algorithms are compared with their accuracy of finding exact data according to the diagnosis report and their execution rate to identify how fast the records are classified. The classification technique based algorithms used in this study are the Naive Bayes Classifier, the C4.5 tree classifier and the K-Nearest Neighbor (KNN) to predict which algorithm is the best suited for classifying any kind of medical dataset. Here the datasets such as Breast Cancer, Iris and Hypothyroid are used to predict which of the three algorithms is suitable for classifying the datasets with highest accuracy of finding the records of patients with the particular health problems. The experimental results represented in the form of table and graph shows the performance and the importance of Naïve Bayes, C4.5 and K-Nearest Neighbor algorithms. From the performance outcome of the three algorithms the C4.5 algorithm is a lot better than the Naïve Bayes and the K-Nearest Neighbor algorithm.

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Sentiment Analysis System for Myanmar News using K Nearest Neighbor and Naïve Bayes

Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering ◽

10.18178/wcse.2020.02.001 ◽

2020 ◽

Keyword(s):

Sentiment Analysis ◽

Nearest Neighbor ◽

Naive Bayes ◽

Naïve Bayes ◽

K Nearest Neighbor ◽

Analysis System

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IntelliFin: Advanced Stock Prediction using Hybrid ML and LSTM Model with Financial Indicators powered by Sentiment Determination using NLP

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.d8437.069520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 428-433

Keyword(s):

Stock Market ◽

Stock Prices ◽

Stock Price ◽

Nearest Neighbor ◽

Learning Algorithm ◽

Majority Voting ◽

Support Vector ◽

K Nearest Neighbor ◽

Financial History ◽

The Right

Stock Trading has been one of the most important parts of the financial world for decades. People investing in the share market analyze the financial history of a corporation, the news related to it and study huge amounts of data so as to predict its stock price trend. The right investment i.e. buying and selling a company stock at the right time leads to monetary benefits and can make one a millionaire overnight. The stock market is an extremely fluctuating platform wherein data is produced in humongous quantities and is influenced by numerous disparate factors such as socio-political issues, financial activities like splits and dividends, news as well as rumors. This work proposes a novel system “IntelliFin” to predict the share market trend. The system uses the various stock market technical indicators along with the company's historical market data trends to predict the share prices. The system employs the sentiment determination of a company's financial and socio-political news for a more accurate prediction. This system is implemented using two models. The first is a hybrid LSTM model optimized by an ADAM optimizer. The other is a hybrid ML model which integrates a Support Vector Regressor, K-Nearest Neighbor classifier, an RF classifier and a Linear Regressor using a Majority Voting algorithm. Both models employ a sentiment analyzer to account for the news impacting the stock prices which is powered by NLP. The models are trained continuously using Reinforcement Learning implemented by the Q-Learning Algorithm to increase the consistency and accuracy. The project aims to support the inexperienced investors, who don't have enough experience in investing in the stock market and help them maximize their profit and minimize or eliminate the losses. The developed system will also serve as a tool for professional investors to help and aid their decision making.

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RB-Bayes algorithm for the prediction of diabetic in Pima Indian dataset

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i6.pp4866-4872 ◽

2019 ◽

Vol 9 (6) ◽

pp. 4866

Author(s):

Rajni Rajni ◽

Amandeep Amandeep

Keyword(s):

Nearest Neighbor ◽

Naive Bayes ◽

Early Stage ◽

Human Life ◽

Naïve Bayes ◽

Support Vector ◽

Pima Indians ◽

K Nearest Neighbor ◽

Fast Pace ◽

Bayes Algorithm

Diabetes is a major concern all over the world. It is increasing at a fast pace. People can avoid diabetes at an early stage without any test. The goal of this paper is to predict the probability of whether the person has a risk of diabetes or not at an early stage. This would lead to having a great impact on their quality of human life. The datasets are Pima Indians diabetes and Cleveland coronary illness and consist of 768 records. Though there are a number of solutions available for information extraction from a huge datasets and to predict the possibility of having diabetes, but the accuracy of their mining process is far from accurate. For achieving highest accuracy, the issue of zero probability which is generally faced by naïve bayes analysis needs to be addressed suitably. The proposed framework RB-Bayes aims to extract the required information with high accuracy that could survive the problem of zero probability and also configure accuracy with other methods like Support Vector Machine, Naive Bayes, and K Nearest Neighbor. We calculated mean to handle missing data and calculated probability for yes (positive) and no (negative). The highest value between yes and no decide the value for the tuple. It is mostly used in text classification. The outcomes on Pima Indian diabetes dataset demonstrate that the proposed methodology enhances the precision as a contrast with other regulated procedures. The accuracy of the proposed methodology large dataset is 72.9%.

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