VMD Stock NYSE: A Hybrid Model for Intraday Stock Price Forecasting

The task of predicting stock prices has become increasingly important in today's fast-paced financial markets. With the rapid development of artificial intelligence and machine learning, researchers have been exploring various methods to improve the accuracy of stock price forecasting. One such method is the use of hybrid models that combine different techniques to leverage their strengths.

In this article, we will introduce a new hybrid model for intraday stock price forecasting using VMD (VMD) as an example. The proposed model combines the power of artificial neural networks and metaheuristic algorithms to predict stock prices with high accuracy.

The use of neural networks in financial time series forecasting has been extensively studied in recent years. For instance, '1' introduced a hybrid model that used artificial neural networks and metaheuristic algorithms to forecast intraday stock price movements. Another study '2' proposed a deep coupled LSTM approach for USD/CNY exchange rate forecasting.

In addition to these models, we can also use other techniques such as CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) and LSTM (Long Short-Term Memory) networks to improve the accuracy of stock price prediction. For example, '3' proposed a financial time series forecasting model based on CEEMDAN and LSTM.

The importance of accurate stock price prediction cannot be overstated. It plays a crucial role in constructing investment decisions or risk hedging. Stock market fluctuations have a significant impact on various economic and financial activities worldwide.

In this paper, we propose a multi-factor two-stage deep learning integrated prediction system based on intelligent optimization and feature clustering to predict stock prices. The proposed model combines the strengths of different techniques to improve the accuracy of stock price forecasting.

The experimental results show that the proposed model outperforms other baseline models in China's two stock markets and the New York Stock Exchange (NYSE). The results indicate that the model has more reliable and better predictive ability than other models.

In conclusion, this article presents a hybrid model for intraday stock price forecasting using VMD as an example. The proposed model combines the power of artificial neural networks and metaheuristic algorithms to predict stock prices with high accuracy. The experimental results demonstrate the effectiveness of the proposed model in improving the accuracy of stock price prediction.

References

'1' Ĭhandar SK, et al. (2021) Hybrid models for intraday stock price forecasting based on artificial neural networks and metaheuristic algorithms.

'2' Ĭao W, Zhu WD, Wang WJ, Demazeau Y, Zhang C (2020) A deep coupled LSTM approach for USD/CNY exchange rate forecasting.

'3' Ĭao J, Li Z, Li J (2019) Financial time series forecasting model based on CEEMDAN and LSTM.

Vmd stock nyse