The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal
and external environmental variables. Artificial intelligence (AI) techniques can detect such …

The literature on stock forecasting using the innovative technique of Artificial Intelligence (AI)
has become overwhelming, making it quite challenging for academics and relevant …

The aim of this study is to identify significant risks for the renewable energy investment
projects. In this regard, a novel hybrid multi-criteria decision-making (MCDM) model has …

Given the high potential benefits and impacts of accurate stock market predictions,
considerable research attention has been devoted to time series forecasting for stock …

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 …

Stock index forecasting is vital for making informed investment decisions. This paper surveys
recent literature in the domain of machine learning techniques and artificial intelligence …

The objective of this study is to explore ways of determining the useful lengths of intervals in
fuzzy time series. It is suggested that ratios, instead of equal lengths of intervals, can more …

In this article, a novel M-factors fuzzy time series (FTS) forecasting model is presented,
which relies upon on the hybridization of two procedures, viz., granular computing and bio …

Fuzzy time series models have been applied to handle nonlinear problems. To forecast
fuzzy time series, this study applies a backpropagation neural network because of its …

Time-series models have been used to make predictions in the areas of stock price
forecasting, academic enrollment and weather, etc. However, in stock markets, reasonable …