Paper Contents
Abstract
Abstract Cryptocurrency price prediction is a form of time series forecasting that is exceptionally complex due to the reliance of crypto prices on various financial, socio-economic, and political factors. Furthermore, minor discrepancies in cryptocurrency price forecasts can lead to substantial losses for companies who rely on these predictions for financial analysis and investment decisions. Recently, artificial intelligence and machine learning techniques have been extensively utilized for cryptocurrency price prediction due to their superior accuracy compared to traditional statistical methods. The suggested methodology utilizes a steepest descent-based scaled backpropagation algorithm in conjunction with data preprocessing via discrete wavelet transform (DWT) for cryptocurrency price forecasting. The proposed system demonstrates a reduced mean square percentage error relative to the previously established technique
Copyright
Copyright © 2024 Anjali Pal. This is an open access article distributed under the Creative Commons Attribution License.
