Nice demo of LSTM: Question about stock-prediction-deep-neural-learning HOT 2 CLOSED

So you need to consider the nature of the LSTM as it will give you an output for next day. This will help you validate your signals.

To test the model output for tomorrow, you would need to feed the model with the most recent data point and use it to make a prediction. Here is an example of how to do this in Python (there should be a prediction example in my solution)

# import necessary libraries
import pandas as pd
from keras.models import load_model

# load the saved model
model = load_model('model.h5')

# load the most recent data point
data = pd.read_csv('data.csv')
latest_data = data.tail(1)

# preprocess the data (normalize, standardize, etc.)
# ... 

# create the input tensor
# ...

# make the prediction
prediction = model.predict(input_tensor)

# print the predicted value
print('Tomorrow\'s stock price prediction:', prediction)

In the code above, you would first load the saved model using the load_model() function from the Keras library. Then, you would load the most recent data point and preprocess it as needed to create the input tensor. Finally, you would use the predict() method of the model to make a prediction for tomorrow's stock price, and print the result to the console.

from stock-prediction-deep-neural-learning.

Thank you. I guess I have to normalize/standardize the same way as you have in your trained model before the prediction.

from stock-prediction-deep-neural-learning.