Alexandro

i already made a fix and created a PR, but no responde, you can get a working version i made here: https://github.com/AlexandroLuis/DDos-Attack

did you try to run `pip install convertdate`? it should work

Hello, the choice depends on your analysis needs, but in essence: `ds`: This column depicts the dates for which you intend to make predictions. `yhat`: In this column, you'll find...

Yes, prophet by default have randomness due to it's bayesian essence. It happens even if MCMC is disabled, so consider Disable Seasonalities, Set Holidays to None, Set Uncertainty to Zero...

Are you using R? if you are, try use `as.list(X)`, X being your variable, if you are using python try: `list(X)`

> Hi @AlexandroLuis, as you can see in the SO question, I am using Python and a typical dataframe for Prophet (with columns `ds` and `y`). Not sure how to...

Are you trying to use seasonality? it worked for me to get these gaps of high sales and low, here's an example: `your_model.add_seasonality(name='weekly', period=30.5, fourier_order=6) # Adjusted Fourier order`

Hello @carlosg-m 1. Prophet currently doesn't require you to manually build any features to account temporal seasonalities, instead it has a built-in mechanism to handle it, automatically detects seasonalities such...

try this: `conda install pytorch torchvision torchaudio -c pytorch`

you can add add_seasonality: `your_model.add_seasonality(name='monthly', period=30.5, fourier_order=6) # Adjusted Fourier order` it will help with the very high and low points, adjust the fourier_order to make it even higher (try...