Time-series forecasting is a significant discipline of data modeling where past observations
of the same variable are analyzed to predict the future values of the time series. Its …

With the advent of technological marvels like global digitization, the prediction of the stock
market has entered a technologically advanced era, revamping the old model of trading …

Stock market prediction has always caught the attention of many analysts and researchers.
Popular theories suggest that stock markets are essentially a random walk and it is a fool's …

Abstract Information gathering has become an integral part of assessing people's behaviors
and actions. The Internet is used as an online learning site for sharing and exchanging …

Smart agricultural sensing has enabled great advantages in practical applications recently,
making it one of the most important and valuable systems. For outdoor plantation farms, the …

Financial Analysis has become a challenging aspect in today's world of valuable and better
investment. This paper introduces the implementation of Recurrent Neural Network (RNN) …

Stock price forecasting has been reported as a challenging task in the scientific and financial
communities due to stock prices' nonlinear and dynamic nature. Machine learning models …

Quantitative trading (QT), which refers to the usage of mathematical models and data-driven
techniques in analyzing the financial market, has been a popular topic in both academia and …

Designing robust frameworks for precise prediction of future prices of stocks has always
been considered a very challenging research problem. The advocates of the classical …

Stock market trading is a major and predominant activity when one talks about the financial
markets. With the inevitable uncertainty and volatility in the prices of the stocks, an investor …