Time Series Prediction using LSTM with PyTorch in Python
Time series data, as the name suggests is a type of data that changes with time. For instance, the temperature in a 24-hour time period, the price of various products in a month, the stock prices of a particular company in a year. Advanced deep learning models such as Long Short Term Memory Networks (LSTM), are capable of capturing patterns in the time series data, and therefore can be used to make predictions regarding the future trend of the data. In this article, you will see how to use LSTM algorithm to make future predictions using time series data.
In one of my earlier articles, I explained how to perform time series analysis using LSTM in the Keras library in order to predict future stock prices. In this article, we will be using the PyTorch library, which is one of the most commonly used Python libraries for deep learning.
Before you proceed, it is assumed that you have intermediate level proficiency with the Python programming language and you have installed the PyTorch library. Also, know-how of basic machine learning concepts and deep learning concepts will help. If you have