LSTM Time Series Prediction Tutorial Using PyTorch in Python - Coronavirus Daily Cases Forecasting

Learn to predict future Coronavirus daily cases using real-world data in this comprehensive tutorial on LSTM Time Series forecasting with PyTorch in Python. Explore the basics of time series analysis, from data loading and preprocessing to model building, training, and evaluation. Dive into practical techniques for handling COVID-19 case data, including data exploration and visualization. Master the process of constructing and training an LSTM model for accurate predictions. Gain hands-on experience in evaluating model performance and using the trained model to forecast future cases. By the end of this tutorial, acquire the skills to apply LSTM-based time series forecasting to real-world pandemic data and other time-dependent scenarios.

Overview of the Coronavirus
Loading the Data
Data Exploration
Data Preprocessing
Building a Model
Training
Evaluation
Using all data for training
Predicting future cases