ARIMA models for time series forecasting in Python
Time series forecasting is a powerful tool used to predict future data points by analyzing past observations. One of the most widely used models for time series forecasting is the ARIMA (AutoRegressive Integrated Moving Average) model. This model is particularly effective for datasets that exhibit autocorrelation or seasonality.
For those new to time series analysis, I recommend checking out my previous articles on time series and ARIMA models available here. In this article, we will delve deeper into ARIMA models, understand their components, and provide a step-by-step guide to building ARIMA models using Python with climatic data of France. This will include practical examples with code snippets and visualizations to help you grasp how ARIMA models work in practice.
