… 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 …

… Financial forecasting problems are usually handled using traditional time … soft computing
integration refers to combining GrC with evolutionary computation or bio-inspired computation …

… financial forecasting, and particularly the prediction of buy opportunities. Financial forecasting
is a vital area in computational finance (… There are numerous works that attempt to forecast …

… role model for soft computing is the human mind. The guiding principle of soft computing is:
… The inclusion of neural computing and genetic computing in soft computing came at a later …

… of softcomputing in time-series forecasting and future trends are presented in this paper. …
significant financial profit using more accurate prediction technology. In order to forecast the …

… censoring the application of soft computing techniques for stock … This survey aptly shows that
soft computing techniques are … “financial stock market forecast using data mining techniques…

… Then, the accuracy of forecasting models based on statistical (stochastic), machine learning
… , and soft/granular RBF network is investigated. To forecast the high-frequency financial data…

… Therefore, by analyzing past data, we can forecast future market trends. The two main …
papers dealing with financial market forecasting. Many of them employ soft computing techniques, …

… Technical indicators are used as inputs, since we attempt to forecast the direction of daily
price change in KOSPI. This study selects 12 technical indicators to form the initial feature …

… biggest problems of typical soft computing approaches in financial forecasting (eg using a
… rule induction based algorithms against financial forecasting benchmarks. Other extensions …