Predicting The Evolution Of Stock Prices Using Long Short-Term Memory (LSTM) Networks With TensorFlow and Keras.
In this tutorial, we will cover the basics of predictive modeling for time series data using recurrent neural networks (RNNs) and long short-term memory (LSTM) networks with TensorFlow and Keras. We’ll start by discussing what time series data is and why it’s important, then we’ll explore the basics of RNNs and LSTMs. We’ll provide some code snippets to help you get started with implementing predictive modelling for time series data. Finally, We will demonstrate how to forecast the movement of the S&P 500 index using an LSTM network.
What is time series data?
Time series data is a type of data that is collected over time. This could be anything from stock prices, weather data, or even social media activity. The data is typically collected at regular intervals, and each data point is associated with a specific time. The goal of predictive modeling for time series data is to use the data from the past to make predictions about the future.
Why is time series data important?
Time series data is important because it can be used to make predictions about the future. For example, if we have data on stock prices from the past, we can use that data to…
