Weather Forecasting with Machine Learning, using Python

Weather Forecasting with Machine Learning, using Python

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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