Menu
First of all, I must say, I'm a beginner to this AI things. I followed most of the tutorials about stock market predictions and all of them are pretty much same. These tutorials using a data set and split in to two sets. First one is Training set and the 2nd one is Test set. They are using Closing price of the stocks to train and make a model. From that model, they insert test data set which contain the closing price and showing two graphs. Then they say the actual and the predicted graphs are pretty much same. The github repo of the tutorial. - https://github.com/surajr/Stock-Predictor-using-LSTM/blob/master/Stock-Predictor-using-LSTM.ipynb This is my question, 1. Why all those tutorials are putting closing price in the testing set also? They are only suppose to insert dates right? Because we are predicting the closing price. This is confusing. Please explain me. 2. No one is telling me how to predict next 7 days values. So if we have a model, how to get next 7 days closing value?
Please help me to clarify this. Thanks a lot.
844
Stock Prices Prediction Using Long Short-Term Memory (LSTM) Model in Python. Long Short-Term Memory (LSTM) is one type of recurrent neural network which is used to learn order dependence in sequence prediction problems. Due to its capability of storing past information, LSTM is very useful in predicting stock prices.
In order to do that, you need to define the outputs as y[t: t + H] (instead of y[t] as in the current code) where y is the time series and H is the length of the forecast period (i.e. the number of days ahead that you want to forecast).
Which machine learning algorithm is best for stock price prediction? Based on experiments conducted in this article, LSTMs seem to be the best initial approach in solving the stock price prediction problem. Other methods can combine features extracted from LSTM or Bi-LSTM models and fed into a classical ANN regressor.
Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices. LSTMs are very powerful in sequence prediction problems because they’re able to store past information. This is important in our case because the previous price of a stock is crucial in predicting its future price.
The results of a the single company LSTM is shown on the left, and the result of the four company LSTM on the right. The first line shows the prediction in the test set, and the second on all the data set. It is not possible to predict the stock market behaviour using only its historical price. The LSTM prediction is far from acceptable.
LSTMs are very powerful in sequence prediction problems because they’re able to store past information. This is important in our case because the previous price of a stock is crucial in predicting its future price. Editor’s note: This tutorial illustrates how to get started forecasting time series with LSTM models.
LSTMs are very powerful in sequence prediction problems because they’re able to store past information. This is important in our case because the previous price of a stock is crucial in predicting its future price.
Take a look at this link. I think it will get you going in the right direction.
https://www.datacamp.com/community/tutorials/lstm-python-stock-market
141
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
