Simple, yet powerful application of Machine Learning for weather forecasting with Python. Learn how to use these models in Python and apply it to real world data. I want to discuss is based on forecasting the average temperature using traditional machine learning algorithms: Auto Regressive Integrated Moving Average models (ARIMA).

Simple, yet powerful application of Machine Learning for weather forecasting with Python

Physicists define climate as a **“complex system”**. While there are a lot of interpretations about it, in this specific case we can consider **“complex”** to be **“unsolvable in analytical ways”**.

This may seems discouraging, **but it actually paves the way to a wide range of numerical algorithms that aim to solve the climate challenges**. With the computational developments of the last years, Machine Learning algorithms are certainly part of them.

The challenge I want to discuss is based on forecasting the average temperature using traditional machine learning algorithms: **Auto Regressive Integrated Moving Average models (ARIMA)**.

While this post doesn’t want to be detailed in terms of the theoretical background, **it does want to be a step-by-step guide on how to use these models in Python and apply it to real world data.**

**So let’s start by describing the Python framework.**

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