In stock market forecasting, the identification of critical features that affect the performance of
machine learning (ML) models is crucial to achieve accurate stock price predictions. Several …

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 load forecasting is the significant task carried out by the electricity providing utility
companies for estimating the future electricity load. The proper planning, scheduling …

This paper presents a new fuzzy time series forecasting model based on technical analysis,
affinity propagation (AP) clustering, and a support vector regression (SVR) model. Technical …

The stock1 market is dynamic, noisy and hard to predict. In this paper, we explored four
machine learning models using technical indicators as input features to predict the price …

The deep learning is a powerful tool for the short term load forecasting. The accurate load
forecasting is an inevitable task in power system for the proper planning of the electricity …

Traditional autoregressive (AR) time series models have been extensively applied to predict
various stationary data sets based on single point data. However, real-world system involves …

Algorithmic trading has become increasingly prevalent in financial markets, and traders and
investors seeking to leverage computational techniques and data analysis to gain a …

This paper proposes and analyses the applicability of integrating Fuzzy C-Means (FCM) and
artificial neural network (ANN) in rainfall forecasting. The algorithm of ANN and FCM …