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

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.
Notebook used in this video: www.kaggle.com/code/robikscub...
Timeline:
00:00 Intro
03:15 Data prep
08:24 Feature creation
12:05 Model
15:35 Feature Importance
17:33 Forecast
Follow me on twitch for live coding streams: / medallionstallion_
My other videos:
Speed Up Your Pandas Code: • Make Your Pandas Code ...
Speed up Pandas Code: • Make Your Pandas Code ...
Intro to Pandas video: • A Gentle Introduction ...
Exploratory Data Analysis Video: • Exploratory Data Analy...
Working with Audio data in Python: • Audio Data Processing ...
Efficient Pandas Dataframes: • Speed Up Your Pandas D...
* KZbin: youtube.com/@robmulla?sub_con...
* Discord: / discord
* Twitch: / medallionstallion_
* Twitter: / rob_mulla
* Kaggle: www.kaggle.com/robikscube
#xgboost #python #machinelearning

[@casperj4784]

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.

[@robmulla]

I absolutely love messages like this. Glad to hear you found this helpful and it gave you the reassurment that things get faster. I can tell you that they do! The goal of my channel is to "spark curiosity in data science" I hope this video did that for you.

[@RaviKumar-uf3eo]

Yes. It is very reassuring, but most probably he would have kept all the things ready.

[@amirghorbani7922]

It is better to use icdst Ai predict lstm model.

[@karishmakapoor4285]

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👍

[@naderbazyari2]

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

[@sevenaac4783]

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

[@flel2514]

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

[@rodolfoviegas8504]

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

[@beckynevin1]

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!

[@luismisanmartin98]

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

[@musicplace9205]

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

[@jelc]

Really well focused and clearly explained. Love your work!

[@robmulla]

I appreciate the feedback Julian

[@MilChamp1]

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

[@robmulla]

Thanks so much. There is so much to cover with time series. I may consider a miniseries that’s a great idea. I’d like to make one on prophet which is a great package for time series forecasting too.

[@zhuoningli]

Hi Rob! Your tutorials help me get a job offer! When I was searching for a job, I received a take-home technical exercise about time series forecasting. I watched this video and finished my exercise. Finally, I got my dream job! Thank you so much!!! I really appreciate your tutorials! 🥰

[@robmulla]

Whoa, I really love hearing stories like this. That's amazing and I wish you the best in the rest of your career.

[@hussamcheema]

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. 😊

[@robmulla]

Thanks! Glad you find it helpful.

[@JacksonWelch]

Love these videos. As a data engineer I love seeing other peoples workflows. Thanks so much for posting.

[@robmulla]

Glad you liked it. Thanks for watching Jackson.

[@ADaBaker95]

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

[@fudgenuggets405]

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

[@Singularitarian]

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

[@NotesandPens-ro9wx]

Man I am seeing this after an year and your teaching style is just hell .. now sub done and will follow you on other things :) for sure

[@nirbhay_raghav]

Hands down, the bestest (if that is a word) video on the entire internet about implementation. No fancy stuff. Not too beginner and toy examples. Hust the right thing what a budding data scientist needs to see. And it is definitely reassuring to see that one can really get better and faster at doing these after a while. It takes me a lot of time reach what you have done in under 30min. Debugging things take a lot of time.

[@robmulla]

I really apprecaite your positive feedback! Glad to hear you find it encouraging that eventually things will get faster.

[@22niloc]

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

[@troy_neilson]

Informative and well-structured. Thanks!

[@TrueTalenta]

I am new to time series and this by far is very informative and quit succinct!

[@a.h.s.3006]

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.

[@robmulla]

Thanks for the comment! Glad you enjoyed the video even though you already have experience with time series. You are 100% correct about the lag features. Check out part 2 where I go over this and a few other topics in detail.

[@adityaraikwar6069]

Being a sort of early intermediate data scientist myself, it's very cool watching him do all these things and the most amazing thing is how everybody's mind works differently and how proficient you become in not only coding but also in approach towards a problem. keep that up man

[@paultvshow]

Hey, have you landed a job in data science field?

[@digitalnomad2196]

also curious to know, recent data science graduate here@@paultvshow

[@sandyattcl]

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

[@robmulla]

Really appreciate that Sandeep. Please share the link with anyone else you think might also like it.

[@69nukeee]

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

[@H99x2]

Incredible content and explanation. You definitely have a knack for this. I subscribed for more videos like this! Thanks :)

[@robmulla]

Thanks for watching and the feedback!

[@lolmatt9]

Very well explained and useful. Thank you!

[@Arieleyo]

Love your videos Rob!! cheers from Argentina ♥

[@robmulla]

Sending my ❤ back to Argentina. Thanks for watching!

[@leo.y.comprendo]

This is incredible! Instantly subscribed!! thanks for your knowldege

[@robmulla]

Thanks for watching!

[@akshaymbhat9144]

Thanks for the wonderful video. It's very insightful ❤️ from India . Keep inspiring and aspiring always!!

[@robmulla]

My pleasure! So happy you liked it!

[@azizbekurmonov6278]

Thanks! Love your explanations.

[@egermani]

Great content! Thanks a lot for the explanations, they are a great incentive to dive deeper into the subject.

[@robmulla]

Glad you think so! My hope is that by making short videos that explain a topic at a high level like this will spark curiosity in people so they will dive deeper into the topic, just like you said.

[@peralser]

Great Video ROB, Thanks for sharing with us!!

[@robmulla]

Thanks for watching!

[@inovosystemssoftwarecompan6724]

short and potent, great fluid presentation !!

[@evandrogaio7003]

Such an excellent video. Thanks for sharing!

[@robmulla]

Glad you liked it!

[@michaelmebratu2921]

What a quality tutorial! Thank you so much

[@robmulla]

Glad you learned something new!

[@user-xr3bc4vn5t]

You have helped me so much with this video, you don't even know!!! Thanks so much :)

[@lovettolaedo223]

I enjoyed watching this as it has given me more insight into prediction. Kindly do a video on GDP growth forecasting using machine learning. Thank you.

[@yosafatrogika3129]

so clear explanation, thanks for sharing!

[@robmulla]

Glad it was helpful!

[@PRATEEK30111989]

I have never seen a better data science video. You are a savant at this

[@kvafsu225]

Great lesson on machine learning. Thank you.

[@robmulla]

Thank you for watching. Share with a friend!

[@super-eth8478]

Dude your channel is a gold mine ..

[@robmulla]

Thanks so much for that feedback. Now share it with anyone you think might appreciate it too!

[@super-eth8478]

@@robmulla Actually I have shared it to my friends . Cheers !

[@prasadjayanti]

Very good explanation.

[@tatulialphaidze90]

Thank you for this tutorial, definitely helped me out

[@robmulla]

Glad it helped!

[@lamborghiniveneno8423]

Simply awesome tutorial😀

[@robmulla]

Thanks so much!

[@Tonitonichoppa_o]

This is the best!! Thank you so much :D 감사합니다!!

[@yourscutely]

Perfectly explained, thanks a lot

[@robmulla]

You are welcome! Glad you found it helpful. Check out parts 2 and 3 and share with a friend!

[@chrispumping]

Very informative and easy to understand tutorial....Thanks you

[@robmulla]

You are welcome! Thanks for watching.

[@romanrodin5669]

Great video! Very clear and easy for understanding! Thanks a lot for clear explanation! I've got a few questions though regarding lagging data for better prediction) will jump into next video, it seems I get an answer there) thanks again!

[@robmulla]

Glad you liked it. Yes, the next video covers it in more detail!

[@tomshaw7179]

Thanks for this video Rob. I am quite new to data science and this was really clear. Have you done a video on optimization maybe using light GBM?

[@anatoliyzavdoveev4252]

Fantastic video tutorial 👏👏🙏

[@ramizajicek]

Thank you for the great presentation

[@robmulla]

I appreciate you watching and commenting. Share with a friend!

[@datalyfe5386]

Just came across your channel, awesome content!

[@robmulla]

Welcome aboard! Glad you like it.

[@demaischta1129]

This is so helpful. Thank You!!

[@liliyalopez8998]

I just started studying ML and this tutorial is super helpful. I would like to see how you would use the model for forecasting future energy consumption though

[@robmulla]

Welcome to the wonderful world of ML Liliya! Yes, I did forget to cover that in detail but I may in a future video. It's just a simple extra step to create the future dates dataframe and run the predict and feature creation on it.

[@ademhilmibozkurt7085]

I love this video. Please make more. Thanks

[@robmulla]

Thanks! I apprecaite the comment. Have you seen the part 2 that I have on this topic?

[@gabrielmoreno2554]

Wow, this is exactly what I needed to learn to improve my COVID death predictor. Great job!

[@robmulla]

So glad you found this helpful. Thanks for watching!

[@nguyenduyta7136]

Best one I ever seen ❤thank so much.

[@robmulla]

So glad you like it. Thanks for the comment.

[@blueradium4260]

Brilliant video, thank you :)

[@robmulla]

Thanks for taking the time to watch.

[@gustavojuantorena]

"And depending who you ask" 🤣Great video!

[@robmulla]

I’m glad you got the reference. I was hoping he would see and appreciate that part of the video.

[@ChrisHalden007]

Great video. Thanks

[@robmulla]

Appreciate that 🙏

[@raasheedpakwashi2961]

LEGEND...no other words needed

[@robmulla]

Thank you 🙏

[@wazzadec16]

FYI for anybody who is doing this recently. The part where combing training set and test set graphic and using a dotted line has to be modified. Before: '01-01-2015' After ax.axvline(x=dt.datetime(2015,1,1) Since matplotlib now needs it in a datetime series. I guess because of changing the index to a t0_datetime format?

[@shrunkhalawankhede2611]

from datetime import datetime ax.axvline(x=datetime(2015,1,1), color='black', ls='--')

[@Burnitall220]

This is incredible!!

[@Lnd2345]

Great video, thanks.

[@robmulla]

Glad you liked it! Thanks for the feedback.

[@Dongnanjie]

Thank you, Rob!

[@MeghaKorade]

Hello Rob, Great tutorial! I have a question - In eval_set you're using [(x_train, y_train), (x_test, y_test)] whereas in most data split practices I've seen validation set separated from training data (which not part of either training or testing set)? Can you please check at timestamp 14:02 ? I'm trying to implement something similar on an interesting dataset and this is a great tutorial!!

[@datasciencesolutions2361]

Great job sincerely!

[@robmulla]

Thanks for the feedback!

[@Mvobrito]

Great video! If the goal was prediction only, and not inference (meaning you don't care about what's driving the energy consumption), you can the energy consumption of the previous days as feature for the model. When predicting consumption at T, you can use T-1, T-2, .. T-x. And even a moving average as feature as well.

[@robmulla]

I totally agree! It all depends on how far in the future (forecasting horizon) you are attempting to predict.

[@lucasfescina]

I love your videos

[@haleemahabulaimon8081]

I really appreciate it

[@revathyb1663]

Great video. How are you taking into account the sequence in information while training the xgb model? Also, what method do you suggest while I deal with multiple time series, meaning say for example I have energy consumption from multiple regions and would like to have predict for each region.

[@adityagavali3158]

Thank for this!

[@muhammadkashif7263]

Amazing season ❤

[@robmulla]

I appreciate the feedback.

[@robmulla]

Thanks!

[@THE8SFN]

great tutorial

[@robmulla]

Thx!

[@massoudkadivar8758]

Perfect job👌

[@mirror1023]

Amazing video

[@robmulla]

Thanks!

[@sunaxes]

Would be useful to look at feature importance at the inflexion point of the test set performance and at the end of training and compare. Features highly ranked in both are the ones useful to understand pattern in data and also also satisfy labelling requirements.

[@a.a.elghawas]

Cool video Rob!

[@robmulla]

Thanks for watching!

[@jiyanshsonofdr.rajesh8516]

Nice explanation..

[@robmulla]

Thanks for liking

[@AQ-jh5fr]

Nice tutorial and when you said quick tutorial you sure meant it xD, I had to pause like a 100 times. but still thanks for the video

[@robmulla]

Glad you liked the video. I'd rather it be too fast than too slow :D - you can always slow down the playback speed if that helps.

[@AisyahAthifa]

Nice tutorial 👍

[@robmulla]

Thank you 👍

[@adityaghai220]

amazing video

[@christianmannke2335]

First of all, thank you for this comprehensive video. It helped me a lot to understand this kind of prediction better. However, what I still don't understand is how can I make predictions on new data that the model hasn't seen before? Let's say I want to make predictions from 2018-08-03 for the next 30 days.

[@wells111able]

thanks a lot ，for a beginner

[@magicdimension6073]

Have you tried SARIMA Models for time series forecasting? I'm curious which perform better. Excelent content Rob!

[@jianyugong8277]

This video is the best I've seen! You are so good at explaining this. Instead of RMSE, could you please advise how I can show the confidence interval of the predictions? I tried to google an answer but nothing worked.

[@robmulla]

Thanks so much for the kind words. Unfortunately with a model like xgboost there are no confidence intervals. However something like an ARIMA or fbprohet model (which i'm planning on making another video about) can give you confidence intervals. Check out part 2 if you haven't already!

[@mohamednedal]

Hi Rob, Great tutorial! Could you please make a tutorial on how to use Shapley values to interpret LSTM models for timeseries forecasting?

[@gui250493]

Well done!

[@robmulla]

Thank you sir!

[@azizbekurmonov6278]

more time series, please

[@kaaz4044]

A question. I see the prediction was done on test data which are already available. This is good to see how accurate the model is but I am wondering how we can use this model (and xgboost in general) to forecast the upcoming years for which we do not have any data.

[@SabahMahjabeenSarwar]

HI thanks for this amazing video. Do you have any video where you have done the improvements that you have mentioned ? Also any link for the code ?

[@abinsharafm.s5168]

I follow you on twitch.. you should definitely do a video on how you setup your system for data science ( I mean you had Linux working with your ide and you were pulling data from the websites (api).. i found that very cool ! )

[@robmulla]

Oh. Great idea! I’ve thought about doing this but need to think more about how to best explain my setup.

[@anwarsaidan3959]

Hi Rob, Thank you very much for this tutorial. When using XGBoost , we don't do these kinds of data prep : scaling, checking for seasonality, filtering outliers ?

[@santinonanini6107]

Should you not split the training data into train and validation sets, such that you can use validation set instead of test set during training ? (when you use "eval_set" parameter ?)

[@neclis7777]

Excellent video ! For weather, I suggest you look into HDD and CDD (heating degree days and cooling degree days) which focus on the amount of heating and cooling rather than the mean temperature.

[@robmulla]

Thanks for the tips! I'm not familiar with those but I will look into it. The one main issue I see when people are training forecasting models like this is using the ground truth weather for future dates- which are not available at the time of prediction. That's why I think it's best to use forecast values from the historic dates.

[@user-cl1eb2hh8o]

謝謝！

[@user-cf5pf7on7k]

Great video - you briefly mentioned stationarity in the beginning, but you didn't actually test for it. This data looks stationary to me, but if it wasn't would that cause a problem? Or is that only an issue with ARIMA models? Thanks!

[@alghanimaa]

excellent introduction, is there an example of this approach with additional exogenous feature variables?