Stock Price Prediction Using LSTM Network

Research in the field of Stock Prediction isn't a new topic of interest. Stock prediction has been focused on since a long period of time.

So what was the means of prediction of the stock prices when we didn't have sequential models that understand the dependency of the current data on the previous data over the time sequence?

Well before the use of Deep Learning in the field of Stock Prediction, the process of building robust models for Stock Price Prediction had started. Several methods had been developed like,

1. Last Value Method
2. Linear Regression
3. Moving Average
4. AutoRegressive Integrated Moving Average (ARIMA)
5. Prophet

But Sequence Models changed everything and led to a massive improvement in state-of-the-art results in the finance industry.

stock_prediction_using_sequence_models.ipynb includes Stock Prediction using an LSTM network

Stock_Prediction_Using_Different_Methods.ipynb includes Stock Price Prediction using methods :

1. Last Value Method
2. Moving Average
3. Linear Regression
4. Exponentially Moving Average