In this literature review, we investigate machine learning techniques that are applied for
stock market prediction. A focus area in this literature review is the stock markets …

Prediction of stock market trends is considered as an important task and is of great attention
as predicting stock prices successfully may lead to attractive profits by making proper …

In recent years, the demand for developing low computational cost methods to deal with
uncertainties in forecasting has been increased. Probabilistic forecasting is a class of …

Abstract Seasonal Auto Regressive Fractionally Integrated Moving Average (SARFIMA) is a
well-known model for forecasting of seasonal time series that follow a long memory process …

In this paper, a novel multi-kernel support vector machine (MKSVM) combining global and
local characteristics of the input data is proposed. Along with, a parameter tuning approach …

The initial aim of this study is to propose a hybrid method based on exponential fuzzy time
series and learning automata based optimization for stock market forecasting. For doing so …

In this study, a new kind of fuzzy set in fuzzy time series' field is introduced. It works as a
trend estimator to be appropriate for fuzzy time series forecasting by reconnoitering trend of …

Long memory time series are stationary processes in which there is a statistical long range
dependency between the current value and values in different times of the series. Therefore …

No campo da previsão de séries temporais os métodos mais difundidos baseiam-se em
predição por ponto. Esse tipo de previsão, no entanto, tem um sério inconveniente: ele não …

The values of some time series in the real world usually change randomly but they may
contain information from history. In these cases, today value can depend not only on …