Successful applications of predictive analysis can be found in finance. For these goals, modern soft computing theories are applied. In contrast to other apps, financial applications have unique characteristics. Forecasting is crucial, particularly in the financial industry because it lowers expenses, which can increase revenues and help businesses win the competition. Due to inescapable changes and expansion in every aspect of life, almost every organization on earth is operating in an unpredictable environment. Forecasting becomes increasingly difficult as a result of these developments, which either directly or indirectly affect stock market values. The demand for trustworthy, economical, and efficient forecasting models is therefore great in order to reduce uncertainty as well as risk in investing in the stock market. Academics and information researchers have developed a variety of time series models for the most precise and perfect future prediction. Financial autoregressive time series models have produced precise prediction-capable predictive models like the autoregressive moving average and autoregressive integrated moving average. To predict the closing value of the BSE100 S&P Sensex record every week and every day, discrete wavelet change and wavelet denoising soft computing models are combined with autoregressive models in the continuing work.
Authors: K R Bhavya, A. Dharmaraj, Prakash Pareek, Manoj Sathe, Mohit Tiwari, Geetha Manoharan
DOI: https://doi.org/10.1109/ic3i59117.2023.10397684
Publish Year: 2023
