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 …

Anomaly detection is a significant problem that has been studied in a broader spectrum of
research areas due to its diverse applications in different domains. Despite the usage of …

In the era of big data, deep learning for predicting stock market prices and trends has
become even more popular than before. We collected 2 years of data from Chinese stock …

Due to the nonlinear modeling capabilities, deep learning prediction networks have become
widely used for smart agriculture. Because the sensing data has noise and complex …

Stocks price prediction is a current hot spot with great promise and challenges. Recently,
there have been many stock price prediction methods. However, the prediction accuracy of …

Internet of Things (IoT)-based Healthcare services, which are becoming more widespread
today, continuously generate huge amounts of data which is often called big data. Due to the …

Early classification of time series has been extensively studied for minimizing class
prediction delay in time-sensitive applications such as medical diagnostic and industrial …

Stock price movement is non-linear and complex. Several research works have been carried
out to predict stock prices. Traditional approaches such as Linear Regression and Support …

Stock price prediction is crucial but also challenging in any trading system in stock markets.
Currently, family of recurrent neural networks (RNNs) have been widely used for stock …

In recent years, scarce water resources became one of the main problems that endanger
human species existence and the advancement of any nation. In this research, smart water …