Instead of apply function, try to use list comprehension at least. It's much faster. Also to extract year, month and etc. you don't need to loop (avoid loop when can). Simply use datetime library and do something like df['year'] = df['date'].dt.year and so own for other calendar related features :)
TypeError: fit() got an unexpected keyword argument 'epochs' . If i remove epochs from fit() then it is working . IS there any altenative way to set epochs?
Great videos! Youre preparation and attention to detail really pays off! Also impressed with your communication of complicated concepts. best of luck ! Will subscribe for more!
Could you please assist me in resolving this issue below? I'm attempting to load the neuralprophet trained model from a different device.encountering a"Error": "[WinError 5] Access is denied:'model trained directory name ' " on model.predict(df) line. How do I figure this out...?
How do you use two or more variables to predict the temperature in neural prophet? for example using "MinTemp", " MaxTemp" and "Temp9am" to predict "Temp3pm"
Hello sir, time series data when cleaning missing value it consider the lag or equal interval?? Like if year 2000 to 2020 but has missing at 2010 and 2019 if we remove this year the sequence of it will loss?? How about outlier?? And so on??❤
Hi Nicholas please can you do a tutorial on the Comparison of the LSTM model in terms of RMSE and spearman correlation under different sizes of training samples
This is good stuff. Well done ! One question. What you have done is univariate, how about if I want to do multivariate prediction i.e. say if I want to predict Temp9am and Temp3pm simultaneously ? Would you say NeuralProphet is only good for cyclical data i.e. temperature, gas usage or sales figures ? Thank you Nicholas.
Hi @Adam, just double checking you're looking to do multi time-series i.e. forecast multiple things at once? Not just adding in new variables which are factored into the forecast i.e. multivariate. Can't find anything to suggest it supports multi series natively but you could easily do this by training two models (that's how I would normally handle it in production). It's great for cyclical data! You can even tune it a ton: neuralprophet.com/hyperparameter-selection/
@@NicholasRenotte That was my initial thought too, but I thought I could shoot two birds with one stone and it might be faster if I could do it in one go especially if I want to run alot of epochs.
mine is giving a error when i run this command plt.plot(melb['Date'], melb['Temp3pm']) NameError Traceback (most recent call last) Input In [9], in () ----> 1 plt.plot(melb['Date'], melb['Temp3pm']) NameError: name 'plt' is not defined
every time i run this command the kernel dies idk why... i am also using the latest version of conda and from there i am running this code on jupyter notebook
another error at epochs while training n = NeuralProphet() model = n.fit(new_column, freq='D', epochs=5000) error: TypeError Traceback (most recent call last) Untitled-1.ipynb Cell 10' in () 1 n = NeuralProphet() ----> 2 model = n.fit(new_column, freq='D', epochs=5000) TypeError: NeuralProphet.fit() got an unexpected keyword argument 'epochs'
unable to initialize epochs value. dont know the actual error it is showing fit() got an unexpected keyword argument 'epochs' this error.please help me out
So... if it's just mimicking the same pattern as earlier what's the use of the timeseries analysis? I could have come up with that "prediction" with zero knowledge of timeseries.
Great video. I am getting an error. m.fit(df, freq='D', epochs=5000) TypeError: fit() got an unexpected keyword argument 'epochs'. Can you help me with this?
Amazing explanation , it's very easy to understand. Is it possible to use Neural Prophet for Multivariate time Series forecast ?. Plz share your suggestions.
Running into a problem where seasonality, yearly shows all years for the x values instead of one year, and weekly also shows all years just like trend. I have no idea what went wrong. Looked up documentation and still can't figure it out. I've used the exact same code you have, and same dataset. Have you ran into this issue before?
Hi!! I do not know why when training the model I get this error: TypeError: fit() got an unexpected keyword argument 'epochs' as if there is no epochs parameter!! can someone help m eon this one??
Hello Sir, thank you for nice video! I am curious that, is prophet model is interpretable in their forecasting (such as feature importance). Or can you recommend some interpretable time series forecasting model? Thanks!!
Hello Nicholas! Great tutorial, thank you! Do you know maybe which exactly algorithm is used in this script? Is thar autoregression, linear regression etc.?
Hey this looks really usefull! I am currently trying to forecast weather dependent systems, I would love to know about a tool as easy and complete as this one that can make use of multiple features! I've tried my hand at coding it all myself using CNN1D, Dense and LSTM layers in Tensorflow & Keras using the Sequential() API, but its a ton of work to get it to work as robustly and efficiently as what you're showing here. If you know of a similarly usable workflow for multivariate timeseries forecasting, please let me know. Oh and great video, you really manage to boil it down to basics while opening up alleyways for further exploration for viewers that already have some experience with this subject matter! :)
Thanks a bunch @Xander, you can actually add future and lagging regressors as part of the library! neuralprophet.com/model/lagged-regressors/ and neuralprophet.com/model/future-regressors/
While trying to install the neuralprophet, I am getting the following error ERROR: Could not find a version that satisfies the requirement torch=1.4.0 (from neuralprophet) (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2, 1.7.1) ERROR: No matching distribution found for torch=1.4.0 (from neuralprophet) can you tell me how to solve it?
use python version 3.8, if you have a python 3.9 downgrade it to 3.8 download the .whl file for torch from the pypi site. Make sure you download the version which is less than 1.7.0, 1.6.0 would do finally use pip to install the package using the downloaded .whl file. use command, pip3 install
@@MrSahalshams python version I am using is 3.8.5 in anaconda with a windows laptop. i have tried to to use pip3 but the following error is shown File "", line 1 pip3 install SyntaxError: invalid syntax when tried with pip , this is shown ERROR: torch-1.6.0-cp38-none-macosx_10_9_x86_64.whl is not a supported wheel on this platform.
@@mdjamalahmedshohan3261 instead of running command on ipython notebook try opening a terminal in jupyter and try the pip3 command. If you get something like command not found or invalid syntax then pip3 is not installed on your environment. Try reinstallinh pip3. Generally while installing python 3 pip3 gets automatically installed as a dependency, i can't think of a reason why pip3 will not be already installed.
@@MrSahalshams pip3 is installed. I tried to install torch with terminal but it is showing this (base) C:\Users\jamal>pip3 install torch-1.6.0-cp38-none-macosx_10_9_x86_64.whl WARNING: Requirement 'torch-1.6.0-cp38-none-macosx_10_9_x86_64.whl' looks like a filename, but the file does not exist ERROR: torch-1.6.0-cp38-none-macosx_10_9_x86_64.whl is not a supported wheel on this platform. Now when I tried installing neuralprophet in terminal the following error is shown: The conflict is caused by: neuralprophet 0.2.7 depends on torch=1.4.0 neuralprophet 0.2.6 depends on torch=1.4.0 neuralprophet 0.2.5 depends on torch=1.4.0 To fix this you could try to: 1. loosen the range of package versions you've specified 2. remove package versions to allow pip attempt to solve the dependency conflict
Heya @Josh, just loop through your 3 different regions and create a separate model for each. It's more multi-series than multivariate. Let me know if you need a hand.
@@joshsmart9178 correct, this is a common approach in the industry particularly when using time series models as each region is likely to have it's own seasonality and trends. A lot of clients I work with have it down to product level forecasts as well with an individual model per SKU.
It's a huge field but I started off by reading through and going through the Tensorflow tutorials. They're super clear and tight examples! www.tensorflow.org/tutorials
Hi can you help me How we can predict for Multivariate data where we have multiple variables to predict not just one using Neural prophet please reply as early as possible i have a assignment till day after tomorrow Thanks
ERROR: Cannot install neuralprophet==0.2.5, neuralprophet==0.2.6 and neuralprophet==0.2.7 because these package versions have conflicting dependencies. how do i solve this..
Hello... Please the RegLoss column that follows the MAE column after the training; what is it? Is it a measure of loss? If so, how does it differ from the SmoothL1Loss and MAE?
Yup, spot on. RegLoss is regularised loss (loss with an applied penalty), SmoothL1Loss is another measure of loss with an L1 penalty applied. MAE is mean absolute error!
Great tutorial! I am planning to work on a time series forecasting application and would like to deploy it using IBM Watson. Is it possible to deploy the neural prophet model on IBM Watson though ?
Hi Sir! This was a great video that made my work simple. But i just want to classify next one week data from forecasted data as cloudy or Sunny or rainy like that. Can you please suggest a way to do these?
Idk if you found an answer, but I would create a separate column to give each type of forecast (sunny, rainy, cloudy, etc.) as a numerical value to feed to a model. Or use something like a Label Encoder. It would likely be not very accurate as opposed to doing something like predicting the amount of precipitation each year or temperature, as types of forecasting can be too inconsistent to predict, but I hope that gets you in the ballpark of what you were asking.
Can you please tell me how to code deep learning ? Because there are a lot of sites,blogs and websites where we can get the mathematics behind deep learning but there is not codeing implementation to that
There's a lot of things you can tackle with deep learning, check this out it's a super basic implementation of deep learning with Tensorflow: ruclips.net/video/6_2hzRopPbQ/видео.html
i am getting this error just by importing the neuralprophet- i wrote - from neuralprophet import NeuralProphet error message - .py:11 in from neuralprophet import NeuralProphet File ~\anaconda3\lib\site-packages euralprophet\__init__.py:9 in from .forecaster import NeuralProphet # noqa: F401 File ~\anaconda3\lib\site-packages euralprophet\forecaster.py:14 in from neuralprophet.conformal import Conformal File ~\anaconda3\lib\site-packages euralprophet\conformal.py:9 in from neuralprophet.plot_forecast_plotly import ( File ~\anaconda3\lib\site-packages euralprophet\plot_forecast_plotly.py:6 in from neuralprophet.plot_model_parameters_plotly import get_dynamic_axis_range File ~\anaconda3\lib\site-packages euralprophet\plot_model_parameters_plotly.py:38 in register_plotly_resampler(mode="auto") NameError: name 'register_plotly_resampler' is not defined
My man you are such an inspiration. All the best to you!
Thank you so much 🙏🙏
@@mohammedmallik3013 ya! Hit me up on LinkedIn for PMs!
I am a Brazilian data science student and you are helping me a lot.
Thank you for the good work!
Great video. Great tutorial. This sort of videos are very hard to prepare, film, edit...so thank you very much for this useful content!!!
Thanks so much @Javier!
Oh My My I'm still here...
You content is gold Sir
Awesome stuff thanks so much @Academy Omen
Why have I only discovered you now? :D where have you been all my life! Your work is amazing, thanks for sharing with the rest of us!
Thank you so much for all the hard work you put into these videos.
Thanks so much @Mohamed!! 🙏 🙏 🙏
15:40 (I us google translate)I use google colab I have a problem when executed it shows "TypeError: fit() got an unexpected keyword argument 'epochs'"
Thanks a lot for this brother! Wish you the best of success in the future.
Thank you soo much @Parakh!
Anyone Getting this error: TypeError: fit() got an unexpected keyword argument 'epochs' ??
Instead of apply function, try to use list comprehension at least. It's much faster. Also to extract year, month and etc. you don't need to loop (avoid loop when can). Simply use datetime library and do something like df['year'] = df['date'].dt.year and so own for other calendar related features :)
Solid suggestion, yeah I'm just a bit lazy most of the time and I'm comfy with apply. But everyone else, definitely listen to this advice!!
thanks
For weather, use icdst Ai predict only it is designed for that.
Love your energy! Excellent explanations, too!
Thanks soo much 🙏🙏!
Can you also make a tutorial on multivariate time series predictions?
TypeError: fit() got an unexpected keyword argument 'epochs' . If i remove epochs from fit() then it is working . IS there any altenative way to set epochs?
I am also facing the same problem. Did you find the solution to the problem?
Great videos! Youre preparation and attention to detail really pays off! Also impressed with your communication of complicated concepts. best of luck ! Will subscribe for more!
Could you please assist me in resolving this issue below?
I'm attempting to load the neuralprophet trained model from a different device.encountering a"Error": "[WinError 5] Access is denied:'model trained directory name ' " on model.predict(df) line.
How do I figure this out...?
thank you so much you saved my diploma, it works, now I started to understand!!!!!
How do you use two or more variables to predict the temperature in neural prophet? for example using "MinTemp", " MaxTemp" and "Temp9am" to predict "Temp3pm"
Take a look at adding lagged or leading regressors, should be in the doco
Hello sir, time series data when cleaning missing value it consider the lag or equal interval?? Like if year 2000 to 2020 but has missing at 2010 and 2019 if we remove this year the sequence of it will loss??
How about outlier?? And so on??❤
Hi Nicholas please can you do a tutorial on the Comparison of the LSTM model in terms of RMSE and spearman correlation under different sizes of training samples
How do you find the accuracy in percentage from MAE? please help
yes bro please help
Could probably do it manually using sklearn or tensorflow metrics.
This is good stuff. Well done !
One question. What you have done is univariate, how about if I want to do multivariate prediction i.e. say if I want to predict Temp9am and Temp3pm simultaneously ? Would you say NeuralProphet is only good for cyclical data i.e. temperature, gas usage or sales figures ? Thank you Nicholas.
Hi @Adam, just double checking you're looking to do multi time-series i.e. forecast multiple things at once? Not just adding in new variables which are factored into the forecast i.e. multivariate. Can't find anything to suggest it supports multi series natively but you could easily do this by training two models (that's how I would normally handle it in production). It's great for cyclical data! You can even tune it a ton: neuralprophet.com/hyperparameter-selection/
@@NicholasRenotte That was my initial thought too, but I thought I could shoot two birds with one stone and it might be faster if I could do it in one go especially if I want to run alot of epochs.
mine is giving a error when i run this command
plt.plot(melb['Date'], melb['Temp3pm'])
NameError Traceback (most recent call last)
Input In [9], in ()
----> 1 plt.plot(melb['Date'], melb['Temp3pm'])
NameError: name 'plt' is not defined
every time i run this command the kernel dies idk why...
i am also using the latest version of conda and from there i am running this code on jupyter notebook
another error at epochs while training
n = NeuralProphet()
model = n.fit(new_column, freq='D', epochs=5000)
error:
TypeError Traceback (most recent call last)
Untitled-1.ipynb Cell 10' in ()
1 n = NeuralProphet()
----> 2 model = n.fit(new_column, freq='D', epochs=5000)
TypeError: NeuralProphet.fit() got an unexpected keyword argument 'epochs'
If i want to find for multiple locations... i should fit the model for each location independently?
unable to initialize epochs value. dont know the actual error
it is showing fit() got an unexpected keyword argument 'epochs' this error.please help me out
So... if it's just mimicking the same pattern as earlier what's the use of the timeseries analysis? I could have come up with that "prediction" with zero knowledge of timeseries.
Thanks for sharing mate, it was really useful! Keep it up with the good job. Subscribed!
Thanks so much @Esteban!!
Great video. I am getting an error.
m.fit(df, freq='D', epochs=5000)
TypeError: fit() got an unexpected keyword argument 'epochs'.
Can you help me with this?
Same thing you found a solution ?
Just remove epochs from the perimeters and then make sure that no rows are being repeated in your data.
Amazing explanation , it's very easy to understand. Is it possible to use Neural Prophet for Multivariate time Series forecast ?. Plz share your suggestions.
Sure can: neuralprophet.com/model/lagged-regressors/
@@NicholasRenotte Can you share a new link, please?
Hey I tried the epochs method but it is showing the fit() got an unexpected key argument
First command error. Can you tell me which version to install for neuralprophet and torch. ERROR: No matching distribution found for torch=1.4.0
What version of Python are you using? Can you try 3.7.3?
Running into a problem where seasonality, yearly shows all years for the x values instead of one year, and weekly also shows all years just like trend. I have no idea what went wrong. Looked up documentation and still can't figure it out. I've used the exact same code you have, and same dataset. Have you ran into this issue before?
Hi!!
I do not know why when training the model I get this error:
TypeError: fit() got an unexpected keyword argument 'epochs'
as if there is no epochs parameter!!
can someone help m eon this one??
you found the soluion?
Just remove epochs from the perimeters and then make sure that no rows are being repeated in your data.
Thanks a lot for the video ! Do you know when we should use Fbprophet versus Neural Prophet ?
I use them interchangably. I'll train them side-by-side and evaluate which model is performing best!
@@NicholasRenotte I think this is a good idea for a video. I am interested in knowing when to use which. Thanks for your videos!!
@@diegobravoguerrero will add it to the list!
Very inspirational bro
Can you please write the code for a specific future date like i want to forecast for 1-nov-2021
Great video! Is it possible to update the model with new data? when working with a sliding window
No skipping ads here
🤗🙏🤗 thanks @Johnathan!
Hello Nick! Thanks so much for this video but is this making use of back propagation technique?
I believe there is still an underlying DNN (haven't checked too much into it) but it would be using back prop for training.
Hello Sir, thank you for nice video! I am curious that, is prophet model is interpretable in their forecasting (such as feature importance). Or can you recommend some interpretable time series forecasting model? Thanks!!
I want to use other features in temperature forecast, humidity, wind speed and so on..... , Do you have any example for that?
Hello Nicholas! Great tutorial, thank you! Do you know maybe which exactly algorithm is used in this script? Is thar autoregression, linear regression etc.?
G'day. Thanks for nice content. I was looking for stock predictions with multiple influence factors such as FX and sector performances. Thanks
There's a vid on that somewhere on the channel, search for ML Trader I think.
is it possible to add multiple variable in this model?
Can hourly seasonality be made? I have hourly data and time is very important to me.
Hey this looks really usefull! I am currently trying to forecast weather dependent systems, I would love to know about a tool as easy and complete as this one that can make use of multiple features! I've tried my hand at coding it all myself using CNN1D, Dense and LSTM layers in Tensorflow & Keras using the Sequential() API, but its a ton of work to get it to work as robustly and efficiently as what you're showing here. If you know of a similarly usable workflow for multivariate timeseries forecasting, please let me know.
Oh and great video, you really manage to boil it down to basics while opening up alleyways for further exploration for viewers that already have some experience with this subject matter! :)
Thanks a bunch @Xander, you can actually add future and lagging regressors as part of the library! neuralprophet.com/model/lagged-regressors/ and neuralprophet.com/model/future-regressors/
@@NicholasRenotte Wow that really improved its performance! Thanks!
Sir Why we did not split our dataset into training and testing set?
Awesome tutorial, very clear and understandable
While trying to install the neuralprophet, I am getting the following error
ERROR: Could not find a version that satisfies the requirement torch=1.4.0 (from neuralprophet) (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2, 1.7.1)
ERROR: No matching distribution found for torch=1.4.0 (from neuralprophet)
can you tell me how to solve it?
use python version 3.8, if you have a python 3.9 downgrade it to 3.8
download the .whl file for torch from the pypi site. Make sure you download the version which is less than 1.7.0, 1.6.0 would do
finally use pip to install the package using the downloaded .whl file. use command, pip3 install
@@MrSahalshams python version I am using is 3.8.5 in anaconda with a windows laptop. i have tried to to use pip3 but the following error is shown
File "", line 1
pip3 install
SyntaxError: invalid syntax
when tried with pip , this is shown
ERROR: torch-1.6.0-cp38-none-macosx_10_9_x86_64.whl is not a supported wheel on this platform.
@@mdjamalahmedshohan3261 instead of running command on ipython notebook try opening a terminal in jupyter and try the pip3 command. If you get something like command not found or invalid syntax then pip3 is not installed on your environment. Try reinstallinh pip3. Generally while installing python 3 pip3 gets automatically installed as a dependency, i can't think of a reason why pip3 will not be already installed.
@@MrSahalshams pip3 is installed. I tried to install torch with terminal but it is showing this
(base) C:\Users\jamal>pip3 install torch-1.6.0-cp38-none-macosx_10_9_x86_64.whl
WARNING: Requirement 'torch-1.6.0-cp38-none-macosx_10_9_x86_64.whl' looks like a filename, but the file does not exist
ERROR: torch-1.6.0-cp38-none-macosx_10_9_x86_64.whl is not a supported wheel on this platform.
Now when I tried installing neuralprophet in terminal the following error is shown:
The conflict is caused by:
neuralprophet 0.2.7 depends on torch=1.4.0
neuralprophet 0.2.6 depends on torch=1.4.0
neuralprophet 0.2.5 depends on torch=1.4.0
To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict
I was able to make it work. Thanks for your comments
what approach would I use in FB Prophet to forecast the revenue of 3 regions of the same company? Do I use multivariate time series modeling?
Heya @Josh, just loop through your 3 different regions and create a separate model for each. It's more multi-series than multivariate. Let me know if you need a hand.
@@NicholasRenotte I must admit, I do need some help on this. Are you saying I should create 3 different forecast models for each of the three regions?
@@joshsmart9178 correct, this is a common approach in the industry particularly when using time series models as each region is likely to have it's own seasonality and trends. A lot of clients I work with have it down to product level forecasts as well with an individual model per SKU.
Where have you learnt to code deep learning ?
Have you self -learnt the coding implementing part?
I suggest you look at Kaggle to begin with. Plenty of tutorials and starting points there. :)
It's a huge field but I started off by reading through and going through the Tensorflow tutorials. They're super clear and tight examples! www.tensorflow.org/tutorials
Hi can you help me How we can predict for Multivariate data where we have multiple variables to predict not just one using Neural prophet
please reply as early as possible i have a assignment till day after tomorrow Thanks
How can we use this model to predict temperature for multiple locations?
What kind of Knowledge base system is it?
How to save forecast results as csv when run with n_lags?
If you've got the results in a data frame you can output the results using your_forecast_df.to_csv('output.csv')
ERROR: Cannot install neuralprophet==0.2.5, neuralprophet==0.2.6 and neuralprophet==0.2.7 because these package versions have conflicting dependencies.
how do i solve this..
Heya @Midnightbrand 0w, can you try installing a single version?
@@NicholasRenotte yikes how do u do that
@@JXEE7 hmm, can you show me what command you ran when installing? Should just be !pip install neuralprophet
@@NicholasRenotte Hey I have the same problem.
I ran this command - !pip install neuralprophet
And it shows that error. How do I fix it?
@@shaunakmitra5050 which error sorry? The conflicting deps one?
its possible to use wiht monthly data?
I facing error kernel restarting, can you please tell how to slove this error??
Cool man! Can you teach how do a quiver with this files? Thanks
Quiver? LIke a Quiver plot?
@@NicholasRenotte Like windy
Hello... Please the RegLoss column that follows the MAE column after the training; what is it? Is it a measure of loss? If so, how does it differ from the SmoothL1Loss and MAE?
Yup, spot on. RegLoss is regularised loss (loss with an applied penalty), SmoothL1Loss is another measure of loss with an L1 penalty applied. MAE is mean absolute error!
@@NicholasRenotte thank you man
Great tutorial! I am planning to work on a time series forecasting application and would like to deploy it using IBM Watson. Is it possible to deploy the neural prophet model on IBM Watson though ?
Hi Sir! This was a great video that made my work simple. But i just want to classify next one week data from forecasted data as cloudy or Sunny or rainy like that. Can you please suggest a way to do these?
Idk if you found an answer, but I would create a separate column to give each type of forecast (sunny, rainy, cloudy, etc.) as a numerical value to feed to a model. Or use something like a Label Encoder. It would likely be not very accurate as opposed to doing something like predicting the amount of precipitation each year or temperature, as types of forecasting can be too inconsistent to predict, but I hope that gets you in the ballpark of what you were asking.
What about resampling after you dropped na?
Can you please tell me how to code deep learning ?
Because there are a lot of sites,blogs and websites where we can get the mathematics behind deep learning but there is not codeing implementation to that
There's a lot of things you can tackle with deep learning, check this out it's a super basic implementation of deep learning with Tensorflow: ruclips.net/video/6_2hzRopPbQ/видео.html
What is the accuracy that you got?
This is a perfect content🤗
That's Great Sir ,Love from india
Can this model accepts more than 2 feilds as an input (i.e. can it do multivariate forcest) ?
And thanks for videos
Great question! Looks like you can: neuralprophet.com/model/lagged-regressors/
@@NicholasRenotte Thanks :)
@@abhimatgupta9693 anytime!
Additive_regression is not working with Neural prophet
Thanks very much brother
Thank you so much
Neural Prophet is worst now not able to change epochs auto set batch size and all
Awesome
It is perfect
Watch before uploading your volume is too low
🙏
I'm
i am getting this error just by importing the neuralprophet-
i wrote - from neuralprophet import NeuralProphet
error message -
.py:11 in
from neuralprophet import NeuralProphet
File ~\anaconda3\lib\site-packages
euralprophet\__init__.py:9 in
from .forecaster import NeuralProphet # noqa: F401
File ~\anaconda3\lib\site-packages
euralprophet\forecaster.py:14 in
from neuralprophet.conformal import Conformal
File ~\anaconda3\lib\site-packages
euralprophet\conformal.py:9 in
from neuralprophet.plot_forecast_plotly import (
File ~\anaconda3\lib\site-packages
euralprophet\plot_forecast_plotly.py:6 in
from neuralprophet.plot_model_parameters_plotly import get_dynamic_axis_range
File ~\anaconda3\lib\site-packages
euralprophet\plot_model_parameters_plotly.py:38 in
register_plotly_resampler(mode="auto")
NameError: name 'register_plotly_resampler' is not defined
its possible to use wiht monthly data?