I want to do something like:
predicted_value = Prophet.predict_some_value('2020-12-12 14:47:00')
where the date is some future date and produce only one value.
Is there a way to predict only the value at a certain date in the future using Facebook Prophet?
I found the answer after reading the documentation more carefully.
First we need to generate a DataFrame containing the date we want to predict the value for:
future_date = pd.DataFrame({'ds':['2020-08-15 7:55:30']})
Then just use the predict method of the fitted model:
forecast = my_model.predict(future_date)
Related
I have created a monthly calendar in tableau that tracks enrollment sign ups for a business's rewards program. What I am trying to do is show the day over day % change between enrollment values. In this specific scenario, I am trying to find the % change between 371 to 499. However, to do this, I believe I first need to create a calculated field for the prior days value, so that I can then use the day over day % formula for all other values. Does anyone have any advice for this particular scenario?
I have tried using the datediff function, amongst other calculated fields, with no luck so far. I want to created a calculated field that will retrieve all "enrollment" values but only for the previous day, so that I can then use the "100*((final-initial)/initial) calculation to calculate the day to day % difference.
You can try this function. I think this might be the calc you need.
(ZN(SUM([Distance])) - LOOKUP(ZN(SUM([Distance])), -1)) /
ABS(LOOKUP(ZN(SUM([Distance])), -1))
You might have to play with the sign to get the result you want. Let me know if that doesn't work.
I have a problem where i want to set a base date to a date time variable in the database. I would then use the base date for my aggregations. And the days subsequent to this base date would be categorised as Day1, Day2 and so on.
So for example, if I want to see cohorts of orders created by the base date - I would want the calculated after this date. I don't want to do this using a date filter.
Image below:
It seems like you could use the DATE function to convert your field, like this:
DATE([Acquisition Date])
If you don't already have the year value stored, but have a method in mind to add it, you could use MONTH([Acquisition Date]) to obtain the month and date and DAY([Acquisition Date]) to obtain the date. You could then use MAKEDATE function to build the date or MAKEDATETIME function to build the datetime value.
Once it's stored as a date value, you could use DATEADD to add and subtract days to it as needed.
There are multiple formulas available for converting Tableau dates, described here: https://help.tableau.com/current/pro/desktop/en-us/functions_functions_date.htm
Is this what you're looking for?
I have a pretty simple .csv table:
I use this table as an input parameter when creating a model in Create ML, where the target is PRICE, CITY and DATE are feature columns.I need to get a price prediction for a giving date in the future for a particular city.
The code below gives a different price for different dates, as it should work, however, it gives the same result regardless of the given city:
let prediction = try? model.prediction(
CITY: name, DATE: date
)
let price = prediction?.PRICE
The price for a given date in the future in Paris should not be equal to the price for the same date in New York.
Do I really need to create 2 different models for each of the cities?
Thank you!
Is this all the data your are giving it? You're going to need to give it a lot more to go off of.
So, it was a bug in the .csv file, it looks like the CITY column was recorded with invisible characters.
Actually I'm working on the Australia weather dataset to predict whether it will rain tomorrow or not?
I'm new to machine learning and I don't know what to do with the date column in my dataset because I know machines only take numerical values.
So plz tell me what I should do with this date how should I deal with it?
You can extract year, month and day number of date as categorical variable. It can be significant what month, year or whether it is the beginning or the end of the month.
If you tell me what kind of language you are using I can help you with code :)
Hey Navin Bondade,
When you are using onehotencoding and supposedly, the column contains too many values, then automatically too many columns pile up in your dataset. So, you have to pick 10 or 15 most frequent values in that particular feature and try to encode them and leave the rest out. I haven't used it personally, but I did see a team win the KDD Orange Cup with this technique.
Happy Learning !!
Please find github link for data set and its info.
I'm working on some bank data set where I need predict the customers who are willing to take the loan(Classification). Can I drop that date column or I need to consider that?
M not sure what type of dates you are referring to, e.g. The date column would make sense if its a Birth Date of the customer, as taking a loan would depend on the person age as well which is conveyed by its birth date. So simply asking should i drop date columns does not make sense. In ML it is always important to consider the context related to column