STOCK PRICE PREDICTION USING SUPPORT VECTOR REGRESSION AND K-NEAREST NEIGHBORS: A COMPARISON
DOI:
https://doi.org/10.29121/ijoest.v6.i4.2022.354Keywords:
Supervised Learning, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Stock MarketAbstract
Supervised Learning is an important type of Machine learning. It includes regression and classification problems. In Supervised learning, Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) can be used for classification and regression. Here, both algorithms are used for regression problem. The stock data is trained by SVR and KNN respectively to predict the stock price of the next day using python tool. Both algorithms are compared and it is observed that the price predicted by SVR is closer as compared to KNN.
Downloads
References
Martin, J. M. (2012). Adaptive Motion Compensation in Radiotherapy. CRC Press. https://www.routledge.com/Adaptive-Motion-Compensation-in-Radiotherapy/Murphy/p/book/9781138374294
algorithmxlab (n.d.). https://algorithmxlab.com/blog/applications-machine-learning-finance/
Singh, A. (2018). A Practical Introduction to K-Nearest Neighbors Algorithm for Regression (with Python code). https://www.analyticsvidhya.com/blog/2018/08/k-nearest-neighbor-introduction-regression-python/
analyticsvidhya (n.d.). https://www.analyticsvidhya.com/blog/2018/10/predicting-stock-price-machine-learningnd-deep-learning-techniques-python/
Bhavsar, S. & Gor, R. (2022). Predicting Restaurant Ratings using Back Propagation Algorithm. International Organization of Scientific Research Journal of Applied Mathematics (IOSR-JM), 18(2), 5-9. https://iosrjournals.org/iosr-jm/papers/Vol18-issue2/Ser-2/C1802021014.pdf
Bhavsar, S. & Gor, R. (2022). Comparison of Back propagation algorithms: Bidirectional GRU and Genetic Deep Neural Network for Churn Customer. IOSR Journal of Computer Engineering (IOSR-JCE), 24(3), 7-12.
Bhavsar, S. & Gor, R. (2022). Stock Price Prediction using Grid Hyper parameter Tuning in Gated Recurrent Unit (In Press). International Journal of Engineering Science Technologies (IJOEST).
corporatefinanceinstitute. (n.d.). https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/financial-markets/
datarevenue. (n.d.). https://www.datarevenue.com/en-blog/machine-learning-for-trading
finance.yahoo (n.d.). https://finance.yahoo.com/quote/yhoo/history/
Ghosh, M. & Gor, R. (2022). Short Message Service Classifier Application using Naïve Bayes algorithm. International Organization of Scientific Research Journal of Computer Engineering (IOSR-JCE).
Gururaj, V. Shriya , V. & Ashwini, K. (2019). Stock Market Prediction using Linear Regression and Support Vector Machines. International Journal of Applied Engineering Research, 14(8), 1931-1934. https://www.ripublication.com/ijaer19/ijaerv14n8_24.pdf
Julius, T. & Seng, H. (2019). LQ45 Stock Index Prediction using k-Nearest Neighbors Regression. International Journal of Recent Technology and Engineering (IJRTE), 8(3). https://www.researchgate.net/profile/Julius-Tanuwijaya/publication/336715759_LQ45_Stock_Index_Prediction_using_k-Nearest_Neighbors_Regression/links/5dae7e5ca6fdccc99d929d4a/LQ45-Stock-Index-Prediction-using-k-Nearest-Neighbors-Regression.pdf
Mohammadreza, G. & Hamidreza , A. (2019). Forecasting Stock Market with Support Vector Regression and Butterfly Optimization Algorithm. Elsevier, 1-11. https://arxiv.org/abs/1905.11462
ritchieng. (n.d.). https://www.ritchieng.com/machine-learning-k-nearest-neighbors-knn/
Sadegh, B. I. & Mohammad, B. (2013). Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background. International Journal of Engineering Research and Applications, 3(5), 605-610. https://www.researchgate.net/publication/304826093_Application_of_K-nearest_neighbor_KNN_approach_for_predicting_economic_events_theoretical_background
Sahoo, P. & Charlapally, K. (2015). Stock Price Prediction Using Regression Analysis. International Journal of Scientific & Engineering Research, 6(3), 1855-1859. https://www.ijser.org/researchpaper/Stock-Price-Prediction-Using-Regression-Analysis.pdf
Samruddhi, K. & Kumar, R. (2020). Used Car Price Prediction using K-Nearest Neighbor Based Model. International Journal of Innovative Research in Applied Sciences and Engineering, 4(3), 686-689. https://www.ijirase.com/assets/paper/issue_1/volume_4/V4-Issue-3-686-689.pdf
searchengineland. (n.d.). Retrieved from https://searchengineland.com/heres-how-i-used-python-to-build-a-regression-model-using-an-e-commerce-dataset-326493
Seethalakshmi, R. (2018). Analysis of stock market predictor variables using Linear Regression. International Journal of Pure and Applied Mathematics, 199(15), 369-378. https://www.researchgate.net/publication/326253896_Analysis_of_stock_market_predictor_variables_using_linear_regression
Shakhla, S. Shah, B. Unadkat, V. Kanani, P. & Shah, N. (2018). Stock Price Trend Prediction Using Multiple Linear Regression. International Journal of Engineering Science Invention, 7(10), 29-33. http://www.ijesi.org/papers/Vol(7)i10/Version-2/D0710022933.pdf
towardsai. (n.d.). https://towardsai.net/p/machine-learning/netflix-stock-prediction-model-a-comparative-study-of-linear-regression-k-nearest-neighbor-knn-4527ff17939b
towardsdatascience. (n.d.). https://towardsdatascience.com/machine-learning-techniques-applied-to-stock-price-prediction-6c1994da8001
Martin, J. M. (2012). Adaptive Motion Compensation in Radiotherapy. CRC Press. https://www.routledge.com/Adaptive-Motion-Compensation-in-Radiotherapy/Murphy/p/book/9781138374294
algorithmxlab (n.d.). https://algorithmxlab.com/blog/applications-machine-learning-finance/
Singh, A. (2018). A Practical Introduction to K-Nearest Neighbors Algorithm for Regression (with Python code). https://www.analyticsvidhya.com/blog/2018/08/k-nearest-neighbor-introduction-regression-python/
analyticsvidhya (n.d.). https://www.analyticsvidhya.com/blog/2018/10/predicting-stock-price-machine-learningnd-deep-learning-techniques-python/
Bhavsar, S. & Gor, R. (2022). Predicting Restaurant Ratings using Back Propagation Algorithm. International Organization of Scientific Research Journal of Applied Mathematics (IOSR-JM), 18(2), 5-9. https://iosrjournals.org/iosr-jm/papers/Vol18-issue2/Ser-2/C1802021014.pdf
Bhavsar, S. & Gor, R. (2022). Comparison of Back propagation algorithms: Bidirectional GRU and Genetic Deep Neural Network for Churn Customer. IOSR Journal of Computer Engineering (IOSR-JCE), 24(3), 7-12.
Bhavsar, S. & Gor, R. (2022). Stock Price Prediction using Grid Hyper parameter Tuning in Gated Recurrent Unit (In Press). International Journal of Engineering Science Technologies (IJOEST).
corporatefinanceinstitute. (n.d.). https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/financial-markets/
datarevenue. (n.d.). https://www.datarevenue.com/en-blog/machine-learning-for-trading
finance.yahoo (n.d.). https://finance.yahoo.com/quote/yhoo/history/
Ghosh, M. & Gor, R. (2022). Short Message Service Classifier Application using Naïve Bayes algorithm. International Organization of Scientific Research Journal of Computer Engineering (IOSR-JCE).
Gururaj, V. Shriya , V. & Ashwini, K. (2019). Stock Market Prediction using Linear Regression and Support Vector Machines. International Journal of Applied Engineering Research, 14(8), 1931-1934. https://www.ripublication.com/ijaer19/ijaerv14n8_24.pdf
Julius, T. & Seng, H. (2019). LQ45 Stock Index Prediction using k-Nearest Neighbors Regression. International Journal of Recent Technology and Engineering (IJRTE), 8(3). https://www.researchgate.net/profile/Julius-Tanuwijaya/publication/336715759_LQ45_Stock_Index_Prediction_using_k-Nearest_Neighbors_Regression/links/5dae7e5ca6fdccc99d929d4a/LQ45-Stock-Index-Prediction-using-k-Nearest-Neighbors-Regression.pdf
Mohammadreza, G. & Hamidreza , A. (2019). Forecasting Stock Market with Support Vector Regression and Butterfly Optimization Algorithm. Elsevier, 1-11. https://arxiv.org/abs/1905.11462
ritchieng. (n.d.). https://www.ritchieng.com/machine-learning-k-nearest-neighbors-knn/
Sadegh, B. I. & Mohammad, B. (2013). Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background. International Journal of Engineering Research and Applications, 3(5), 605-610. https://www.researchgate.net/publication/304826093_Application_of_K-nearest_neighbor_KNN_approach_for_predicting_economic_events_theoretical_background
Sahoo, P. & Charlapally, K. (2015). Stock Price Prediction Using Regression Analysis. International Journal of Scientific & Engineering Research, 6(3), 1855-1859. https://www.ijser.org/researchpaper/Stock-Price-Prediction-Using-Regression-Analysis.pdf
Samruddhi, K. & Kumar, R. (2020). Used Car Price Prediction using K-Nearest Neighbor Based Model. International Journal of Innovative Research in Applied Sciences and Engineering, 4(3), 686-689. https://www.ijirase.com/assets/paper/issue_1/volume_4/V4-Issue-3-686-689.pdf
searchengineland. (n.d.). Retrieved from https://searchengineland.com/heres-how-i-used-python-to-build-a-regression-model-using-an-e-commerce-dataset-326493
Seethalakshmi, R. (2018). Analysis of stock market predictor variables using Linear Regression. International Journal of Pure and Applied Mathematics, 199(15), 369-378. https://www.researchgate.net/publication/326253896_Analysis_of_stock_market_predictor_variables_using_linear_regression
Shakhla, S. Shah, B. Unadkat, V. Kanani, P. & Shah, N. (2018). Stock Price Trend Prediction Using Multiple Linear Regression. International Journal of Engineering Science Invention, 7(10), 29-33. http://www.ijesi.org/papers/Vol(7)i10/Version-2/D0710022933.pdf
towardsai. (n.d.). https://towardsai.net/p/machine-learning/netflix-stock-prediction-model-a-comparative-study-of-linear-regression-k-nearest-neighbor-knn-4527ff17939b
towardsdatascience. (n.d.). https://towardsdatascience.com/machine-learning-techniques-applied-to-stock-price-prediction-6c1994da8001
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Ghosh Madhumita, Ravi Gor
This work is licensed under a Creative Commons Attribution 4.0 International License.
