Time series weather forecasting is a popular application for data analysis and machine learning. In this blog post, we will show how to use Python for time series weather forecasting. Weather forecasting is a difficult task that involves the use of complex models and a lot of computing power. There are two main approaches in weather forecasting: data-driven and physics-based. The data driven approach uses historical data to make predictions about the future weather conditions. For example, a weather model can use historical data to make predictions of temperature and rainfall for the next 3 days. A physics-based approach uses the principles of physics to make predictions about the future weather conditions. For example, a weather model can consider atmospheric and oceanic current movements to predict temperature and pressure at different locations in the world.

Here in this project we have:

-Preprocessed our data from our dataset using the Pandas library. -Trained a time series forecasting model to predict temperature using the model. -Forecasted the temperature into the future. -Performed some exploratory data analysis on our data, trained our model, and finally made the predictions.