Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4136
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dc.contributor.authorKumar, Priyanshu-
dc.contributor.authorHota, Lopamudra-
dc.contributor.authorTikkiwal, Vinay Anand-
dc.contributor.authorKumar, Arun-
dc.date.accessioned2023-12-18T04:43:45Z-
dc.date.available2023-12-18T04:43:45Z-
dc.date.issued2023-11-
dc.identifier.citationInternational Conference on Machine Learning and Data Engineering (ICMLDE), UPES, Dehradun, India, 23-24 November 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4136-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractPredicting stock prices is a well-known and significant problem. We can learn about market behaviour over time and identify trends that might not have been seen without an effective stock prediction model. Machine learning will be a useful approach to solving this issue with the increased processing capacity of computers. Behavioural economics also asserts that the investments made by investors depend on their emotions so psychological theories can also be applied to explain their behaviour and its impact on the market. Combining the analysis of these behavioural patterns with the use of historical financial data sets can result in an approach for accurate stock price predictions. The primary focus of the proposed model is on comparing different models to provide a comparative analysis of the results provided by models used in the literature. The paper provides insight into the improvisation of the current techniques and how different parameters and different error analysis techniques can be implementeden_US
dc.subjectStock Marketen_US
dc.subjectDeep Learningen_US
dc.subjectXAIen_US
dc.subjectForecastingen_US
dc.subjectTime-Seriesen_US
dc.titleAnalysing Forecasting of Stock Prices: An Explainable AI Approachen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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