Forecasting with the FB Prophet Model

In this video I show how you can use facebook's prophet model to easily do time series forecasting in python. This model is very powerful because it uses an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.

Timeline:
00:00 Intro
00:50 Data and Imports
03:43 Features and EDA
05:54 Test Split
06:49 Train and Predict
10:01 Evaluate Forecast
15:01 Adding Holidays
19:19 Make Future Dataframe

My other videos:

#fbprophet #python #machinelearning

[mschuer]

Hey Rob, another amazing video, and great overview of Prophet. There are a couple of time series projects i want to tackle and I am going to take a crack and them using Prophet. Just want to say that I think this video is going to help me out a lot. Thanks for the effort you put into making these. Please keep them coming, they are more useful than you know.

[ChristenKult]

Thanks a lot, I love your forecasting content, especially with that dataset!
Would love to see some more models in the future. Wish you the best!

[marcellinusokoh]

Great work... I am actually working on a comparative analysis of ARIMA, prophet, and xgboost in time series forecasting for my project

[davoodastaraky]

Amazing video Rob. I hope you continue making these videos and sharing your knowledge. You are also a great teacher.

[hasanovmaqsud]

I see Rob has new video about time series forecasting. I put like and comment automatically. That's how it works! 😉

[sandipbez]

Thank you for sharing the knowledge, this helps me a lot

[Lucia-elex]

Me encantan tus vídeos. Eres un excelente profesional en tu campo. Gracias por compartir tus conocimientos con la comunidad de Youtube :)

[TylerMacClane]

Surprisingly close. I watch your videos and everything is clear.

[devnull]

Your videos are pure gold, thank you.

[anthonyfrancisco]

I'm learning a ton, thanks for the great content!

[AM-evew]

Awesome work!

You made me interested in ML.

I'm PhD in Physics, and I always preferred to avoid ML during University days, but you, and your work made it so interesting for me!

Thanks for your work.

[codepour]

Video is awesome as always! Quick, easy to follow and interesting. Thank you! btw, where did you get that t-shirt?

[dfdf]

I wish I would have come across this channel months ago, huge fan, amazing work.

[abdessamedbouchena]

Thank you very much this is great work and very useful i hope just make new video about LSTM model

[juanpabloaguirre]

thanks teacher, greettings from Chile, gracias profesor

[feap]

Hey Rob, Awesome Video.
I think you need to take note that if you try to run your kagle notebook with a different data set, it's impossible to evaluate the error metrics because the y_pred array is the same size as the data set. I had to write a few more lines to extract just the last x values I needed.
Thanks again

[giordano_vitale_uni]

Great video, thanks for letting us learn this model!

I have a question. At minute 10:13, the forecast interval includes negative values for the dependent variable: how is it possible? and how can we prevent it?

[marcounipd]

Extra like for the “Model Train” meme ❤

[eduardomanotas]

Hey rob nice video!!
Could you please have another video about using panel datasets and implement AB testing as well.

[cemberkay]

Thanks for the explanation. When using the xgboost model, can we include holidays in the model using the method you showed in this video?