Prediction of Stock Prices using Prophet Model with Hyperparameters tuning

Title

Prediction of Stock Prices using Prophet Model with Hyperparameters tuning

Creator

Maheshwari A.; Malhotra A.; Tuteja S.; Ranka M.; Basha M.S.A.

Description

As part of the data analytical process, predicting and time - series are crucial. In academics and financial research, anticipating share prices is a prominent and significant subject. A share market would be an uncontrolled environment for anticipating shares since there are no fundamental guidelines for evaluating or anticipating share prices there. As a result, forecasting share prices is a difficult time-series issue. fundamental, technical, time series predictions and analytical strategies are just a few of the various techniques and approaches that machine learning uses to execute stock value predictions. This article implements the stock price prediction, Researchers compared the model of the prophet with the tuned model of the prophet. By utilizing the tuning of hyperparameters using parameter grid search to improve the performance of the model accuracy for the best prediction. The findings of the study demonstrated that tuned model of the prophet with hyperparameters tuning which results in model accuracy and based on the experimental findings mean squared error (MSE) and mean absolute percentage error (MAPE) has significant improvement. 2022 IEEE.

Source

2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

forecast; hyperparameter tuning; prophet model; share prices; time-series

Coverage

Maheshwari A., School of Commerce, Finance and Accountancy Christ (Deemed to Be University), Department of Commerce, Ghaziabad, India; Malhotra A., School of Commerce, Finance and Accountancy Christ (Deemed to Be University), Department of Commerce, Ghaziabad, India; Tuteja S., Indira Institute of Management Pune (IIMP), Maharashtra, Pune, India; Ranka M., Arch Research College for Higher Education, Jaipur, India; Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to Be University), Bengaluru, India

Rights

Restricted Access

Relation

ISBN: 978-166545342-4

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/NKCon56289.2022.10126796

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163063097&doi=10.1109%2fNKCon56289.2022.10126796&partnerID=40&md5=2a3c3f6228de9eb36506ccbaf6051c4c