… covering financial time series forecasting and trading systems using traditional soft computing
… , focusing on DL implementations of financial time series forecasting. Our motivation is two-…

… We propose a new VMD-based financial data prediction model. In this model, … financial
data forecasting model; (2) A guideline on the parameter selection of VMD to process financial …

… forecasting using soft computing techniques but it is limited to particular class of soft computing…
, traditional statistical and soft computing approaches used for solving financial problem, …

… on DL implementations for financial applications. A substantial portion of the computational
intelligence for finance research is devoted to financial time series forecasting. However, we …

… Forecasting of fast fluctuated and high-frequency financial … abrupt stochastic variation of the
financial market. Stock market … There are different techniques for forecast prices in the time-…

… Experiments demonstrate that in the majority of cases, soft computing techniques perform
better than conventional models. They deliver better results than a prediction system with …

… However, the empirical forecasting usually has poor capabilities of … forecasting. Aiming to
improve the accuracy of the empirical forecasting, some statistical-based financial forecasting …

… specific financial application fields except forecasting research. … the preliminary research
on financial forecasting using ML … Thus, nonlinear, soft computing models may resolve this …

… We focus on DL applications in finance. Table 1 analyzes corresponding studies along
different dimensions related to the forecasting setting, underlying data, and neural network …

… a new forecasting hybrid model for financial time series data … the data more suitable for
forecast. The improvement of the … to generated different forecasting results and combined by the …