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2023 | OriginalPaper | Buchkapitel

Stock Price Prediction for Market Forecasting Using Machine Learning Analysis

verfasst von : Vivek Kumar Prasad, Darshan Savaliya, Sakshi Sanghavi, Vatsal Sakariya, Pronaya Bhattacharya, Jai Prakash Verma, Rushabh Shah, Sudeep Tanwar

Erschienen in: Proceedings of Fourth International Conference on Computing, Communications, and Cyber-Security

Verlag: Springer Nature Singapore

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Abstract

A common opinion of stock market in society is that the stock market is either insecure to invest in or troublesome to trade, so many people are disinterested. The stock market is a marketplace that facilitates the acquisition and sale of business stock. The stock index has its unique value on each stock exchange. The index is the average value calculated by aggregating the prices of several stocks. The forecast of the entire stock market is time-varying and depends on the stock price movement. The seasonal variance and steady flow of any index helps both professional as well as amateur investors understand and decide whether to invest in shares and the stock market. Individuals and businesses alike can be affected significantly by the stock market. As a consequence, accurately anticipating stock movements can lower the risk of losing money while increasing profits. To address these issues, time series analysis will be the most effective tool for predicting the trend or even the future. This article represents the comparison of LSTM, ARIMA and SARIMAX models for stock price prediction. It is observed that error for ARIMA model is less as compared to SARIMAX model.

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Metadaten
Titel
Stock Price Prediction for Market Forecasting Using Machine Learning Analysis
verfasst von
Vivek Kumar Prasad
Darshan Savaliya
Sakshi Sanghavi
Vatsal Sakariya
Pronaya Bhattacharya
Jai Prakash Verma
Rushabh Shah
Sudeep Tanwar
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-99-1479-1_35