Stock Price Prediction

Problem Statement ->

We implement a Recurrent Neural Network with multiple LSTM (Long Short Term Memory) layers, and use this to predict the stock. In this particular example, I am using a dataset with google price details, and so it will predict the google stock price. But the dataset, can be changed easily to predict any stock.

Improving model ->

1. Getting more training data: we trained our model on the past 5 years of the Google Stock Price but it would be even better to train it on the past 10 years.

2. Increasing the number of timesteps: the model remembered the stock prices from the 60 previous financial days to predict the stock price of the next day. That’s because we chose a number of 60 timesteps (3 months). You could try to increase the number of timesteps, by choosing for example 120 timesteps (6 months).

3. Adding some other indicators: if you have the financial instinct that the stock price of some other companies might be correlated to the one of Google, you could add this other stock price as a new indicator in the training data.

4. Adding more LSTM layers: we built a RNN with four LSTM layers but you could try with even more.

5. Adding more neurons in the LSTM layers: we highlighted the fact that we needed a high number of neurons in the LSTM layers to respond better to the complexity of the problem and we chose to include 50 neurons in each of our 4 LSTM layers. You could try an architecture with even more neurons in each of the 4 (or more) LSTM layers.