Multiple Time Series Forecasting With Scikit-Learn

You got a lot of time series data points and want to predict the next step (or steps). What should you do now? Train a model for each series? Is there a way to fit a model for all the series together? Which is better?

I have seen many data scientists think about approaching this problem by creating a single model for each product. Although this is one of the possible solutions, it's not likely to be the best.

Here I will demonstrate how to train a single model to forecast multiple time series at the same time. This technique usually creates powerful models that help teams win machine learning competitions and can be used in your project.

And you don’t need deep learning models to do that!

Timestamps
0:00 Intro
1:28 Melt the data, stack the series
7:18 Split the data
10:29 Set-up a 1-step target
13:57 Create 4 fundamental features (feature engineering)
26:16 Choose an evaluation metric
31:34 Establish a baseline
35:18 Train the model
37:34 Evaluate the model
39:11 Extend the model to multi-step forecasting
43:04 Forecast new data
45:37 Next steps

Code: https://github.com/ledmaster/english_tutorials/tree/main/multiple_time_series

Timestamps:
0:00 Intro

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// SUPPORT THE CHANNEL ?❤️

Sign up for a Coursera course:
https://imp.i384100.net/EaDmQe

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// SOCIAL MEDIA

LinkedIn: https://www.linkedin.com/in/mariofilho/
Kaggle: https://kaggle.com/mariofilho
Twitter: https://twitter.com/mariofilhoml
Blog: https://forecastegy.com

Some links above can be from partnerships where I get a commission if you buy a product, without any additional cost to you. Thanks for the support!