SAHAANN: A NOVEL EVOLUTIONARY ARTIFICIAL NEURAL NETWORK FOR IMPROVED FINANCIAL TIME SERIES FORECASTING
Here, we introduce SAHAANN, a new kind of hybrid forecasting model that combines ANN with SAHA, a recently created meta-heuristic, and other artificial neural network (ANN) techniques. Optimizing the ANN's weights and biases is what SAHA does. We tested this concept by simulating two popular st...
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2025-03-01
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| Series: | Proceedings on Engineering Sciences |
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| Online Access: | https://pesjournal.net/journal/v7-n1/66.pdf |
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| author | Abdul Khadeer Vanaparthi Kiranmai B. Suvarnamukhi Ayaz Mohiuddin Balika Mahesh P M Suresh J Arthy Sasikumar A N Sudersan Behera Mohd Ayaz Uddin |
| author_facet | Abdul Khadeer Vanaparthi Kiranmai B. Suvarnamukhi Ayaz Mohiuddin Balika Mahesh P M Suresh J Arthy Sasikumar A N Sudersan Behera Mohd Ayaz Uddin |
| author_sort | Abdul Khadeer |
| collection | DOAJ |
| description | Here, we introduce SAHAANN, a new kind of hybrid forecasting model that combines ANN with SAHA, a recently created meta-heuristic, and other artificial neural network (ANN) techniques. Optimizing the ANN's weights and biases is what SAHA does. We tested this concept by simulating two popular stock indices. To measure how well the model works, we mostly utilize MSE, RMSE, and MAPE. To pass the test, all you have to do is guess how much each stock index will be worth in one day and 10 days. We were able to see how the results of training the ANN model with different metaheuristics, such as the genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), fireworks algorithm (FWA), and chemical reaction optimization (CRO). In order, they are GAANN, PSOANN, DEANN, FWAANN, and CROANN. For every model, we perform an exhaustive evaluation. The SAHAANN model has a perfect record of success in the lab. |
| format | Article |
| id | doaj-art-8089246b0a324d2eaee3bc5fb6d4ae45 |
| institution | OA Journals |
| issn | 2620-2832 2683-4111 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj-art-8089246b0a324d2eaee3bc5fb6d4ae452025-08-20T01:57:35ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112025-03-017164565810.24874/PES07.01D.020SAHAANN: A NOVEL EVOLUTIONARY ARTIFICIAL NEURAL NETWORK FOR IMPROVED FINANCIAL TIME SERIES FORECASTINGAbdul Khadeer0https://orcid.org/0009-0005-9002-3488Vanaparthi Kiranmai 1https://orcid.org/0009-0003-9330-2189B. Suvarnamukhi 2https://orcid.org/0009-0003-7901-7277Ayaz Mohiuddin 3https://orcid.org/0009-0004-4507-0210Balika Mahesh 4https://orcid.org/0009-0001-3051-3849P M Suresh 5https://orcid.org/0009-0008-6126-2449J Arthy 6https://orcid.org/0009-0008-6126-2449Sasikumar A N 7https://orcid.org/0000-0003-1957-6168Sudersan Behera 8https://orcid.org/0000-0002-0249-6738Mohd Ayaz Uddin 9https://orcid.org/0009-0002-5223-4303 Faculty of CSE, Deccan Collage of Engineering and Technology, Hyderabad, India Faculty of CSE-AIML, Guru Nanak Institutions Technical Campus, Hyderabad, India Faculty of CSE, Sreyas Institute of Engineering and Technology, Hyderabad , India Faculty of Information Technology, University of Technology and Applied Sciences - Salalah, Sultanate of Oman Faculty of AIML, Sphoorthy Engineering College, Nadergul, Hyderabad, India Faculty of AIML, Sphoorthy Engineering College, Nadergul, Hyderabad, India Faculty of CSE, SRM Institute of science and Technology, Ramapuram, Chennai, India Faculty of CSE, Panimalar engineering college, Chennai, Tamilnadu , India Faculty of CSE, Siddhartha Institute of Technology & Sciences, Narapally, Hyderabad, India Faculty of AIML, Sphoorthy Engineering College, Nadergul, Hyderabad, India Here, we introduce SAHAANN, a new kind of hybrid forecasting model that combines ANN with SAHA, a recently created meta-heuristic, and other artificial neural network (ANN) techniques. Optimizing the ANN's weights and biases is what SAHA does. We tested this concept by simulating two popular stock indices. To measure how well the model works, we mostly utilize MSE, RMSE, and MAPE. To pass the test, all you have to do is guess how much each stock index will be worth in one day and 10 days. We were able to see how the results of training the ANN model with different metaheuristics, such as the genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), fireworks algorithm (FWA), and chemical reaction optimization (CRO). In order, they are GAANN, PSOANN, DEANN, FWAANN, and CROANN. For every model, we perform an exhaustive evaluation. The SAHAANN model has a perfect record of success in the lab.https://pesjournal.net/journal/v7-n1/66.pdfartificial neural networkset algebra-based heuristic algorithmfireworks algorithmchemical reaction optimizationstock forecastingfinancial time series forecasting |
| spellingShingle | Abdul Khadeer Vanaparthi Kiranmai B. Suvarnamukhi Ayaz Mohiuddin Balika Mahesh P M Suresh J Arthy Sasikumar A N Sudersan Behera Mohd Ayaz Uddin SAHAANN: A NOVEL EVOLUTIONARY ARTIFICIAL NEURAL NETWORK FOR IMPROVED FINANCIAL TIME SERIES FORECASTING Proceedings on Engineering Sciences artificial neural network set algebra-based heuristic algorithm fireworks algorithm chemical reaction optimization stock forecasting financial time series forecasting |
| title | SAHAANN: A NOVEL EVOLUTIONARY ARTIFICIAL NEURAL NETWORK FOR IMPROVED FINANCIAL TIME SERIES FORECASTING |
| title_full | SAHAANN: A NOVEL EVOLUTIONARY ARTIFICIAL NEURAL NETWORK FOR IMPROVED FINANCIAL TIME SERIES FORECASTING |
| title_fullStr | SAHAANN: A NOVEL EVOLUTIONARY ARTIFICIAL NEURAL NETWORK FOR IMPROVED FINANCIAL TIME SERIES FORECASTING |
| title_full_unstemmed | SAHAANN: A NOVEL EVOLUTIONARY ARTIFICIAL NEURAL NETWORK FOR IMPROVED FINANCIAL TIME SERIES FORECASTING |
| title_short | SAHAANN: A NOVEL EVOLUTIONARY ARTIFICIAL NEURAL NETWORK FOR IMPROVED FINANCIAL TIME SERIES FORECASTING |
| title_sort | sahaann a novel evolutionary artificial neural network for improved financial time series forecasting |
| topic | artificial neural network set algebra-based heuristic algorithm fireworks algorithm chemical reaction optimization stock forecasting financial time series forecasting |
| url | https://pesjournal.net/journal/v7-n1/66.pdf |
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