A comprehensive survey on higher order neural networks and evolutionary optimization learning algorithms in financial time series forecasting
S Behera, SC Nayak, AVSP Kumar - Archives of Computational Methods …, 2023 - Springer
The financial market volatility has been a focus of study for experts over past decades. While
stockbrokers and investors expect reliable projections of future stock indices, it instead …
stockbrokers and investors expect reliable projections of future stock indices, it instead …
Developing a novel stock index trend predictor model by integrating multiple criteria decision-making with an optimized online sequential extreme learning machine
It has always been the goal of many researchers to gain a thorough understanding of the
patterns in the stock market and forecast the trends it will follow. The use of an advanced …
patterns in the stock market and forecast the trends it will follow. The use of an advanced …
Elitist-opposition-based artificial electric field algorithm for higher-order neural network optimization and financial time series forecasting
This study attempts to accelerate the learning ability of an artificial electric field algorithm
(AEFA) by attributing it with two mechanisms: elitism and opposition-based learning. Elitism …
(AEFA) by attributing it with two mechanisms: elitism and opposition-based learning. Elitism …
Investing in Pairs of Precious Metals: Portfolio Theory Application.
RKMOL RAJ, P DLOUHÝ, V KOVAČ… - Acta Montanistica …, 2023 - search.ebscohost.com
The paper aimed to examine the price development of gold, silver, and platinum from 1
January 2019 to 27 November 2023. The aim was also to predict the price development of …
January 2019 to 27 November 2023. The aim was also to predict the price development of …
Improved firefly based pi-sigma neural network for gold price prediction
S Behera, AVSP Kumar… - 2023 OITS International …, 2023 - ieeexplore.ieee.org
Predicting financial time series is a formidable challenge, given the dynamic nonlinearity
and data complexity inherent in such data. In response to this challenge, our study presents …
and data complexity inherent in such data. In response to this challenge, our study presents …
Forecasting Financial Commodities Using an Evolutionary Optimized Higher-Order Artificial Neural Network
S Behera, AVS Pavan Kumar, SC Nayak - International Conference on …, 2023 - Springer
The dynamic nonlinearity approach and data series make financial time series prediction
difficult. This research suggests the hybridization of an improved Firefly Algorithm (IFFA) and …
difficult. This research suggests the hybridization of an improved Firefly Algorithm (IFFA) and …
Predicting Stock Market Prices Using a Hybrid of High-Order Neural Networks and Barnacle Mating Optimization
S Behera, AVS Pavan Kumar, SC Nayak - International Conference on …, 2023 - Springer
Predicting stock market movements presents a formidable challenge due to the inherent
nonlinearity and ever-changing nature of financial markets. In this research endeavor, we …
nonlinearity and ever-changing nature of financial markets. In this research endeavor, we …