{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:21:06Z","timestamp":1777044066580,"version":"3.51.4"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T00:00:00Z","timestamp":1625702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>With the advent of machine learning, numerous approaches have been proposed to forecast stock prices. Various models have been developed to date such as Recurrent Neural Networks, Long Short-Term Memory, Convolutional Neural Network sliding window, etc., but were not accurate enough. Here, the aim is to predict the price of a stock and compare the results obtained using three major algorithms namely Kalman filters, XGBoost and ARIMA. Kalman filters are recursive and use a feedback mechanism to perform error correction. This correction makes them best suited for making accurate predictions as they can factor in the market volatility, whereas XGBoost is a promising technique for datasets that are nonlinear and can gather knowledge by detecting patterns and relationships in the data. XGBoost is also capable of capturing the time dependency of features efficiently. ARIMA refers to an Auto Regressive Integrated Moving Average model that has become very popular in recent times. It is mostly used on time series data and works by eliminating its stationarity. Finally, a hybrid model combining Kalman filters and XGBoostis discussed and a comparison of the results of each of the four models, are made to provide a better clarity for making investments by forecasting the price of a stock.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab008","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T19:14:12Z","timestamp":1622488452000},"page":"1338-1351","source":"Crossref","is-referenced-by-count":12,"title":["Prediction of Stock Prices Using Statistical and Machine Learning Models: A Comparative Analysis"],"prefix":"10.1093","volume":"65","author":[{"given":"Venkata Vara","family":"Prasad","sequence":"first","affiliation":[{"name":"Sri Sivasubramaniya Nadar College of Engineering, Department of CSE, Kalavakkam, Chengalpattu District, Chennai 603110, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Srinivas","family":"Gumparthi","sequence":"additional","affiliation":[{"name":"Sri Sivasubramaniya Nadar School of Management, Kalavakkam, Chengalpattu District, Chennai 603110, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lokeswari Y","family":"Venkataramana","sequence":"additional","affiliation":[{"name":"Sri Sivasubramaniya Nadar College of Engineering, Department of CSE, Kalavakkam, Chengalpattu District, Chennai 603110, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S","family":"Srinethe","sequence":"additional","affiliation":[{"name":"Sri Sivasubramaniya Nadar College of Engineering, Department of CSE, Kalavakkam, Chengalpattu District, Chennai 603110, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R M","family":"Sruthi Sree","sequence":"additional","affiliation":[{"name":"Sri Sivasubramaniya Nadar College of Engineering, Department of CSE, Kalavakkam, Chengalpattu District, Chennai 603110, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K","family":"Nishanthi","sequence":"additional","affiliation":[{"name":"Sri Sivasubramaniya Nadar College of Engineering, Department of CSE, Kalavakkam, Chengalpattu District, Chennai 603110, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,7,8]]},"reference":[{"key":"2022051812595198800_ref1","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.eswa.2018.06.032","article-title":"Forecasting stock market crisis events using deep and statistical machine learning techniques","volume":"112","author":"Chatzis","year":"2018","journal-title":"Expert Syst. 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Sci."},{"key":"2022051812595198800_ref6","doi-asserted-by":"crossref","DOI":"10.31274\/rtd-180813-7911","article-title":"Kalman filtering approach to market price forecasting","author":"Rankin","year":"1986"},{"key":"2022051812595198800_ref7","doi-asserted-by":"crossref","first-page":"26","DOI":"10.3390\/ijfs7020026","article-title":"Stock market analysis: a review and taxonomy of prediction techniques","volume":"7","author":"Shah","year":"2019","journal-title":"Int. J. Finan. Stud."},{"key":"2022051812595198800_ref8","volume-title":"Stock trend prediction: Based on machine learning methods. escholarship.org","author":"Song","year":"2018"},{"key":"2022051812595198800_ref9","first-page":"1","article-title":"ANN model to predict stock prices at stock exchange markets","author":"Wanjawa","year":"2014","journal-title":"arXiv preprint arXiv"},{"key":"2022051812595198800_ref10","volume-title":"In 5th International Symposium on Knowledge Acquisition and Modeling (KAM 2015)","author":"Yan","year":"2015,"},{"key":"2022051812595198800_ref11","first-page":"113","article-title":"Impact of earning per share and price earnings ratio on market price of share: a study of auto sector in India","volume":"5","author":"Kumar","year":"2017","journal-title":"Int. J. 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Finance Market."},{"key":"2022051812595198800_ref16","doi-asserted-by":"crossref","first-page":"309","DOI":"10.5539\/mas.v12n11p309","article-title":"ARIMA model in predicting banking stock market data","volume":"12","author":"Almasarweh","year":"2018","journal-title":"Modern Appl. Sci."},{"key":"2022051812595198800_ref17","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1109\/UKSim.2014.67","volume-title":"2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation","author":"Ariyo","year":"2014,"},{"issue":"425","key":"2022051812595198800_ref18","doi-asserted-by":"crossref","first-page":"777","DOI":"10.2307\/2234974","article-title":"Can agents learn to rational expectations? Some results on convergence and stability of learning in the UK stock market","volume":"104","author":"Timmerman","year":"1994","journal-title":"Econ. 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