prophet | Financial markets analysis framework for programmers | Cryptocurrency library

The code:

I've seen that I can change my interval level prior to fitting but programmatically that feels like a LOT of expensive trial and error. Is there a way to estimate the confidence bound using only the information from the model parameters and the forecast data frame?

Something like:

All I am doing is running this piece of code. I have used this dataset https://www.kaggle.com/camnugent/sandp500 but tried a few different ones, and all of them returned this message.

• A datetime column with the following format %Y-%m-%d
• An int column

They represent a day and an hour. I want to merge because then I want to give to prophet so I need to convert to this format:

I have a fairly tall dataset (roughly between 1,000 and 5,000 rows) with two columns; one for category and the other for values. The data varies; there might be 10 categories, maybe 100, or any other number:

etc.

I would like to create ranges based on the values in Column A that only include the values in Column B, using the value of Column A as the name of the range.

So for example:

• Range Name = "Category 1" ; Range Values { Value 1, Value 2, Value 3}
• Range Name = "Category 2" ; Range Values { Value 4, Value 5, Value 6}
• Range Name = "Category 3" ; Range Values { Value 7, Value 8, Value 9, Value 10}
• etc.

I've tried a number of things, but I'm such a VBA novice that I can't tell what is an effective way to approach this solution and fear I keep making wrong turns in the approach.

I thought perhaps trying to find the next distinct value in Column A might be a good idea paired with the offset function, but I can't get much further than simply creating and naming the range of categories to look through in Column A. I'm sure this isn't necessary, but I am definitely not a VBA expert.

Running forecasts on new data is just too slow and I would like to use Forecast to train models and eventually deploy them somewhere else.

I make the future dataframe with make_future_dataframe(periods=365). I pandas.concat the future and train DFs (as pandas.merge cuts off the blank 5th year) future_regressors = pandas.concat([future, train[['f1', 'f2', 'f3', 'f4', 'f5']]], axis=1). Prophet however wants the 5 regressors to have values in the 5th year (which it expresses by complaining about the NaNs from the beginning of the 5th year), but the data doest exist in the training set.

What's the best way to go about this?