LSTM Stock Market Prediction with TensorFlow Part 4: Functional Model

In the previous parts of this series, we have learned about LSTM (Long Short-Term Memory) networks and how they can be used for stock market prediction using TensorFlow. In this part, we will take a look at using different data sources and building a functional model for our stock market prediction project.

Using Different Data Sources

When it comes to predicting stock market prices, using different data sources can provide valuable insights and improve the accuracy of our predictions. Some of the commonly used data sources for stock market prediction include:

• Historical stock prices
• Financial indicators (e.g., PE ratio, EPS)
• Market sentiment data (e.g., news sentiment, social media sentiment)
• Macroeconomic indicators (e.g., GDP growth, inflation)

By combining these different data sources, we can create a more comprehensive dataset for training our LSTM model and make more informed predictions about future stock prices.

Functional Model

Now that we have collected and preprocessed our data from different sources, we can start building our LSTM model. In this part, we will be using a functional model in TensorFlow to create a more flexible and modular architecture for our stock market prediction project.

The functional API in TensorFlow allows us to define complex neural network architectures by connecting layers in a more flexible manner. This enables us to build models with multiple inputs and outputs, as well as incorporate features like skip connections and parallel processing.

Here is an example of how we can create a simple LSTM model using the functional API in TensorFlow:

        
import tensorflow as tf
from tensorflow.keras.layers import Input, LSTM, Dense
from tensorflow.keras.models import Model

# Define input layer
inputs = Input(shape=(T, D))

# Define LSTM layer
x = LSTM(128)(inputs)

# Define output layer
outputs = Dense(1)(x)

# Create model
model = Model(inputs, outputs)
model.compile(optimizer='adam', loss='mse')
        
    

In this example, we define an input layer with a shape of (T, D), where T is the time steps and D is the number of features in our input data. We then add an LSTM layer with 128 units followed by a dense output layer with 1 unit for predicting the stock price. Finally, we compile the model with the Adam optimizer and mean squared error loss function.

By using the functional API in TensorFlow, we can easily experiment with different network architectures, add more layers, and fine-tune our model to improve its performance for stock market prediction.

Conclusion

In this part of the LSTM stock market prediction series, we have explored the use of different data sources and built a functional model using the TensorFlow functional API. By incorporating multiple data sources and leveraging the flexibility of the functional API, we can create more sophisticated models for predicting stock market prices with LSTM networks.