How to Predict Stock Prices with LSTM

A Practical Example of Stock Prices Predictions with LSTM using Keras TensorFlow

In a previous post, we explained how to predict stock prices using machine learning models. Today, we will show how we can use advanced artificial intelligence models such as the Long-Short Term Memory (LSTM). In the previous post, we have used the LSTM models for Natural Language Generation (NLG) models, like the word-based and the character-based NLG models.The LSTM ModelLong short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning having feedback connections. Not only can process single data points such as images, but also entire sequences of data such as speech or video. For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition, machine translation, anomaly detection, time series analysis, etc.

he LSTM models are computationally expensive and require many data points. Usually, we train the LSTM models using GPU instead of CPU. Tensorflow is a great library for training LSTM models.

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