Time Series Forecasting with XGBoost - Use python and machine learning to predict energy consumption

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In this video tutorial we walk through a time series forecasting example in python using a machine learning model XGBoost to predict energy consumption with python. We walk through this project in a kaggle notebook (linke below) that you can copy and explore while watching.

Timeline:
00:00 Intro
03:15 Data prep
08:24 Feature creation
12:05 Model
15:35 Feature Importance
17:33 Forecast

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#xgboost #python #machinelearning

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A comprehensive yet succinct tutorial. And, having only just finished my Data Science degree, I found it very reassuring to see that you do get faster and more proficient with time.

casperj

Amazing flow, comprehensive yet smooth. Detailed yet generic. I love the way you think and your float across the entire process. I did this project myself and thoroughly enjoyed it. Cant wait to apply this to other datasets. A Big thumps up👍

karishmakapoor

Second time watching this and doing every step on my notebook as Rob goes through the task. I am still blown away by the intricacy of his approach and how he investigates the case. fascinating how he makes it look effortless. Many thanks

naderbazyari

Hi Rob, I am a fresh data science graduate, and I find this tutorial very well done and very helpful for those that approach TS for the first time as well as for those that want to refresh the topic

flel

Thank you for teaching me. It allows me to understand the time series XGBoost in the shortest time.

sevenaac

Thanks! one of the best video I've ever seen. Simple, clear and overall why each concept is used for.

musicplace

As someone just getting introduced to time series analysis, this video was gold, thank you for making it!

luismisanmartin

Really well focused and clearly explained. Love your work!

jelc

Amazing. We've learnt time series prediction only by statistical methods and/or making ML models to act like ARIMA - making lags for feed them. This approuch very interesting and intuitive. Thanks, Rob

rodolfoviegas

Best video on the subject I've found so far!

ADaBaker

Wow! I'm trying to get up to speed on XGBoost, so I clicked on this video. There are a lot of meh data science tutorials out there, so it was such a treat to come across this one after slogging through youtube. I immediately subscribed and am headed to your channel to watch more videos on time series prediction!

beckynevin

Very illuminating! Learned a whole lot in just 23 minutes.

Singularitarian

Such an amazing video, thank you Rob and keep 'em coming! ;)

nukeee

I like this dude's videos. They are informative and to the point.

fudgenuggets

Informative and well-structured. Thanks!

troy_neilson

I love your content. Liked the video before watching it because I know this is gonna be a great tutorial.
Thanks for making these tutorials. 😊

hussamcheema

This was a very nice introduction to this topic. You might consider turning this into a miniseries, since it's such a large topic; the next video might be on how to create the best cross-validation splits for timeseries

MilChamp

I'm getting to know Time Series and your vid has loads of great starter points.

niloc

what an amazing tutorial! I just had to give a thumbs up even before finishing the video.

sandyattcl

I worked with time series before, and this tutorial is very thorough and well made.

Additional features you could think about are lag/window features, where you basically try to let the model cheat from the previous consumption, by giving it a statistical grouping of previous values, let's say the mean of consumption within a window of 8 hours, or by outright giving the previous value (lag), let's say the actual consumption 24 hours ago.
This will greatly improve performance, because it helps the model to go follow the expected trend.

a.h.s.

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