Stock price forecasting is a complex task that has been a challenge for even the most experienced market professionals. Recently, deep learning techniques have shown promising results in this field, but they still face limitations when it comes to predicting long-term trends. In this article, we propose an improved model called ResNLS, which combines the strengths of two popular architectures: CNN and LSTM. Our goal is to provide a more accurate and efficient way of forecasting stock prices.
CNNs vs. LSTMs
Computer vision and pattern recognition have been around for decades, and CNNs have proven themselves to be highly effective in image classification tasks. However, when it comes to sequential data like time series, LSTMs are the go-to choice due to their ability to capture long-term dependencies. The problem is that these two architectures were designed for different purposes, making it difficult to combine them in a way that exploits their strengths.
ResNLS: A Hybrid Approach
Our proposed model, ResNLS, combines the spatial and temporal features of CNNs and LSTMs, respectively. This allows us to take advantage of both worlds by leveraging the strengths of each architecture. Spatial features are useful for capturing local patterns in time series data, while temporal features help identify long-term trends. By fusing these two types of features, ResNLS can provide a more comprehensive understanding of stock price movements.
How ResNLS Works
The ResNLS model consists of three main components: Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a hybrid attention mechanism. The CNNs are used to extract spatial features from the input time series data, while the LSTMs handle the temporal aspects. The attention mechanism allows the model to focus on the most important parts of the input data, which can be adjusted based on the desired forecasting horizon.
Advantages of ResNLS
One of the main advantages of ResNLS is its ability to capture both local and long-term patterns in stock price movements. By combining CNNs and LSTMs, we can identify patterns that may not be apparent when using a single architecture. Additionally, the hybrid attention mechanism allows us to adaptively focus on the most relevant input features, which can improve forecasting accuracy.
Conclusion
In this article, we proposed an improved model for stock price forecasting called ResNLS. By combining CNNs and LSTMs with a hybrid attention mechanism, ResNLS can capture both local and long-term patterns in time series data. Our experiments show that ResNLS outperforms traditional RNNs and other deep learning models in terms of accuracy and computational efficiency. We believe that our proposed model has the potential to revolutionize the field of stock price forecasting by providing more accurate predictions and helping investors make better decisions.
