Nifty stock prediction using elasticnet and LSTM (Record no. 20100)
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| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20231108151418.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 231108b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 22088 |
| Author | Sai, Navya |
| 245 ## - TITLE STATEMENT | |
| Title | Nifty stock prediction using elasticnet and LSTM |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.25(4), Jul-Aug |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Haryana |
| Name of publisher, distributor, etc. | IOSR - International Organization of Scientific Research |
| Year | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 9-14p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | In The Financial Markets, Predicting Stock Prices Is Essential For Risk Management And Investing Strategies. The Long Short-Term Memory (LSTM) Algorithm And Deep Learning Techniques Are Used In This Study To Provide A Novel Method For Forecasting Nifty Stock Prices. The Study Makes Use Of A Broad Dataset From Yahoo Finance That Spans Five Years, From 2017 To 2023.Data Pre-Processing, Which Includes Data Cleansing, Normalisation, And Feature Engineering, Is The First Step In The Research Process. The Performance Of Elastic Net And LSTM, Two Distinct Stock Price Prediction Systems, Is Then Contrasted. Our Findings Show That The LSTM Algorithm Fared Better Than The Elastic Net Algorithm, Capturing The Underlying Patterns And Trends Inside The Nifty Stock Values With More Precision. The LSTM Method, Which Is Well Known For Its Capacity To Simulate Long-Term Relationships And Sequential Data, Was Successful In Reproducing The Intricate Dynamics Of The Stock Market. The LSTM Effectively Learned The Temporal Patterns And Correlations In The Nifty Stock Prices By Utilising The Memory Cell Structure Of The System, Leading To Higher Prediction Accuracy. To Evaluate The Effectiveness Of The LSTM Model, Evaluation Measures Including R2 Square, Mean Absolute Error, Mean Squared Error, And Root Mean Squared Error Were Computed. The Model's Ability To Correctly Forecast Nifty Stock Prices Is Demonstrated By The Obtained R2 Square Value Of 0.9902756561864434 And Low Error Metrics, Including A Mean Absolute Error Of 10.723074522467424. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 22089 |
| Co-Author | Lekha, A C |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Gurgaon International Organization of Scientific Research (IOSR) |
| Title | IOSR Journal of Computer Engineering (IOSR-JCE) |
| International Standard Serial Number | 2278-8727 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://www.iosrjournals.org/iosr-jce/papers/Vol25-issue4/Ser-1/B2504010914.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Permanent Location | Current Location | Shelving location | Date acquired | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 2023-11-08 | 2023-1546 | 2023-11-08 | 2023-11-08 | Articles Abstract Database |