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A brief review of modeling approaches based on fuzzy time series
P Singh - International Journal of Machine Learning and …, 2017 - Springer
Recently, there seems to be increased interest in time series forecasting using soft
computing (SC) techniques, such as fuzzy sets, artificial neural networks (ANNs), rough set …
computing (SC) techniques, such as fuzzy sets, artificial neural networks (ANNs), rough set …
Develo** a deep learning framework with two-stage feature selection for multivariate financial time series forecasting
Intelligent financial forecasting modeling plays an important role in facilitating investment-
related decision-making activities in financial markets. However, accurate multivariate …
related decision-making activities in financial markets. However, accurate multivariate …
[HTML][HTML] Designing fuzzy time series forecasting models: A survey
Time Series is an orderly sequence of values of a variable in a particular domain.
Forecasting is a challenging task in the area of Time Series Analysis. Forecasting has a …
Forecasting is a challenging task in the area of Time Series Analysis. Forecasting has a …
[PDF][PDF] A tutorial on fuzzy time series forecasting models: recent advances and challenges
Time series forecasting is a powerful tool in planning and decision making, from traditional
statistical models to soft computing and artificial intelligence approaches several methods …
statistical models to soft computing and artificial intelligence approaches several methods …
Predictive intelligence using ANFIS‐induced OWAWA for complex stock market prediction
Traditional time series prediction methods are unable to handle the complex nonlinear
relationship of a large data set. Most of the existing techniques are unable to manage …
relationship of a large data set. Most of the existing techniques are unable to manage …
Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks
In recent years, time series forecasting studies in which fuzzy time series approach is utilized
have got more attentions. Various soft computing techniques such as fuzzy clustering …
have got more attentions. Various soft computing techniques such as fuzzy clustering …
A novel forecasting method based on multi-order fuzzy time series and technical analysis
Financial trading is one of the most common risk investment actions in the modern economic
environment because financial market systems are complex non-linear dynamic systems. It …
environment because financial market systems are complex non-linear dynamic systems. It …
Forecasting in non-stationary environments with fuzzy time series
Time series arise in many fields of science such as engineering, economy and agriculture to
cite a few. In the early 1990's the so called Fuzzy Time Series were proposed to handle …
cite a few. In the early 1990's the so called Fuzzy Time Series were proposed to handle …
[PDF][PDF] Application of data mining techniques in stock markets: A survey
One of the most important problems in modern finance is finding efficient ways to summarize
and visualize the stock market data to give individuals or institutions useful information about …
and visualize the stock market data to give individuals or institutions useful information about …
Fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering
Fuzzy time series approaches have being increasingly attracted researchers' attentions. The
procedures on fuzzy time series actually consist of three stages; fuzzification, determination …
procedures on fuzzy time series actually consist of three stages; fuzzification, determination …