Flower-like NiCo2O4consisting of nanosheets are synthesized by hydrothermal technique and subsequently surface-modified with a TiO2ultrathin layer by a hydrolysis process at low temperature.
In general, it is hard to forecast the prices the stock prices due to the stochastic fluctuations. This research aims to describe the process to use time series models, multifactorial regression, and machine learning to predict stock prices. ARIMA and EGARCH models are frequently used time series models to predict stock prices. Least-squares linear regression model, Lasso, and Polynomial Linear Regression model predict well in statistical regression methods. RNN and LSTM have higher prediction accuracy. Overall, time series models, statistical regression, and machine learning all can predict stock prices. Summarizing the different methods or models to forecast stock market trending can help investors to prepare relevant investing strategies. These results shed light on guiding further exploration of
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.