| Type of Material: | Thesis |
| Title: | Prediction and trend analysis of financial time series deep learning and kernel adaptive filtering perspective |
| Researcher: | Mishra, Shambhavi |
| Guide: | Ahmed, Tanveer | Mishra, Vipul Kumar |
| Department: | School of Computer Science Engineering and Technology |
| Publisher: | Bennett University |
| Place: | Greater Noida |
| Year: | 2022 |
| Language: | English |
| Subject: | Computer Science | Computer Science Theory and Methods | Engineering and Technology | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; School of Computer Science Engineering and Technology, Bennett University, Greater Noida; 2022 |
| Fulltext: | Shodhganga |
| 003 | IN-AhILN | |
| 005 | 2026-10-14 11:56:24 | |
| 008 | __ | 261014t2022||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_464145 |
| 040 | __ | |dIN-AhILN|aBENN_201310 |
| 041 | __ | |aeng |
| 100 | __ | |eResearcher|aMishra, Shambhavi |
| 110 | __ | |aSchool of Computer Science Engineering and Technology|0U-0890|dGreater Noida|bBennett University|ein |
| 245 | __ | |aPrediction and trend analysis of financial time series deep learning and kernel adaptive filtering perspective |
| 260 | __ | |aGreater Noida|c2022|bBennett University |
| 300 | __ | |dDVD |
| 502 | __ | |d2022|bPhD|cSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida |
| 518 | __ | |d2023|oDate of Award |
| 518 | __ | |d2018|oDate of Registration |
| 518 | __ | |d2022|oDate of Completion |
| 520 | __ | |aStock price prediction is one of the important applications of time series analysis. Its success stems from its ability to reduce asset management costs, market impacts, and volatility risks. Stock price prediction is a significant challenge, and a plethora of techniques have been investigated. Nevertheless, a true solution is yet to be found. Despite a lot of effort, research has demonstrated that forecasting a stock s price is difficult, especially when considering the nonlinear and non-stationary behavior of financial time series. The prediction of stock prices is traditionally done with regression and classification, thereby requiring a large set of batch-oriented and independent training samples. The work presented in this thesis takes on this challenge and present an automated framework to address two important issues in the field: i) Stock trend analysis and ii) Stock price prediction. For the former issue, we propose a 3D convolutional neural network based approach to classify the directional trends |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aComputer Science |
| 653 | __ | |aComputer Science Theory and Methods |
| 653 | __ | |aEngineering and Technology |
| 700 | __ | |aAhmed, Tanveer|eGuide |
| 700 | __ | |aMishra, Vipul Kumar|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/512233|yShodhganga |
| 905 | __ | |afromsg |
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