Forecasting with the FB Prophet Model

How can we determine test split point ?

Is it possible to get mean error less than 100? I’ve tried a couple tutorials with weather and it worked really nice with mean error less than 10. But with electricity consumption I could get to 1000 as my best result, after a million of modification.

It is complicated trust me you need to limit you speed

from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error

great video, thank you

features_and_target contains the time series features.
However, the dataset used to predict in prophet model i.e. pjme_train_prophet did not contain the time series features. Is that deliberate or we do need to add features when trainingt the model.

AttributeError: 'DatetimeProperties' object has no attribute 'weekofyear'
The weekofyear attribute has been deprecated in newer versions of pandas. Instead, you should use the isocalendar() method to access the week of the year.

Hi just one doubt, does this model only work for datetime format data's? or does it work for a data which has just year column only? because i have a dataset which has years only. and if this doesn't work means can anyone please tell any other model which is good?

how about applying time series cross validation into fb prophet models?

thanks for the tutorials. How can we improve the model's accuracy?

First of all, thank you for this nice video!

I have a question why you created the features_and_target dataframe. I know that the features are helpful when we train models like XGBoost, but here we only used the ds and y column. I'd just like to understand what was the reason why you created it and if there are any ways to use this dataframe with Prophet?

Awesome video !! However, somehow I am getting the following error:

fig, ax = plt.subplots(figsize=(10, 5))
ax.scatter(datos_test.index, datos_test['Hs(m)'], color='r')
fig = model.plot(datos_test_forecast, ax=ax)
ax.set_xbound(lower='01-01-2019',
upper='02-01-2019')
ax.set_ylim(0, 60000)
plot = plt.suptitle('January 2019 Forecast vs Actuals')

ConversionError: Failed to convert value(s) to axis units: '01-01-2019'

is it necesary to make circular the time variables? as a loop for the 24 hours, or the 7 days of the week, the 12 months and the 365 days of the year

Music sucks.

great tutorial

Thanks a lot from Bangladesh

Excellent video!

Just started with Prophet to model dispensing figures based on previous data but having problems, any help would be appreciated...Thanks Nick

Hi Rob, great video I am wondering if you stopped making machine learning videos as this video was 1 year old

i dont know but when having this data is very predictable , tuning these models can be done, but the more real world application majority of these models fail... we need to keep feeding new data, new features and yet the model misses.

puting exampes everywhere like this, I think they are just bread crumbs to feed our imagination or desire or to get people excited but very quick you hit the wall and realize that this forcasting anything is a taugh task...

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

Hello Rob, great video, thanks for sharing! This was my introduction to the Prophet model, and loved your explanation, crystal clear sa always!

I have a quick question though: if I understand correctly, Prophet should work as a regression model given this very problem, and hence we could calculate R2 as well as metric along with RSE, MAE and MAPE. Is it recommendable to do it, or it makes no sense for this particular model? What's your take?

Thanks a lot!

I am facing challenges running the notebook on google colab especially on the Time series features section ,please help anyone

hey rob a quick question, why not use the train_test_split() from sklearn?

Need your help. I do not have time components in my datasets, i only have date and the consumption data...how to choose the right model for this.. should i choose Xgboost or prophet or Arima...

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

What is the default number of periods predicted?

when I try to install fbprophet on my local machine, I get an error saying "ModuleNotFoundError: No module named Cyphon"

thanks teacher , greettings from Chile, gracias profesor

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

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

Excellent. But graphs are extremely awful! Can you improve them?

Have you published the LSTM video? It is surprising that the initial XGBoost model with held-out test dataset performed the best given the simplicity and minimum effort.

Like the explanations

can you do with neural prohet?

Hello, in which python version can we run the prophet library? I'm developing on Anaconda-Spyder but I get different errors each time.

Good job! Nive vid!👏

Hey, so i am getting yhat as negative since in my data the trend is that number of user decreased in last 4 months, what should i do?

Hi Rob, thanks for sharing this. One question - what if i have many products to forecast sales, do i need to train/run model for each product? Or i could do this in one shot meaning ONE model to cover all products?

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.

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

Great Video! When I try to filter my Date Range for when you're doing "January 2015 Predicted vs Actual" I get an error like

Failed to convert value(s) to axis units: array([....]) -- Any idea what I could be doing wrong here?

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

Wonderful. Can you do one tutorial one Neural Prophet as well, lots of thanks

Your videos are pure gold, thank you.

Hi Rob, thnks fro great video. Why did you add exrta features to data like hour, day of week? You did not use them as a regressor. They are already embeded in date. I didn't understand why you did add like that.