Closed FancyPandaSoftworks closed 6 years ago
Prophet only uses timestamps as a predictor. I'd recommend taking the output prediction of prophet and putting that into another model, like scikit-learn or statsmodels
Hey there,
I checked these models and I realize they are python codes.
Since I have never coded in Python, it would take too much time now to learn it.
Do you have any recommendation to do this in R?
Nelson Auner notifications@github.com 於 2018年9月25日 週二 02:07 寫道:
Prophet only uses timestamps as a predictor. I'd recommend taking the output prediction of prophet and putting that into another model, like scikit-learn or statsmodels
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This would be possible, although I have my reservations. If you have a time-independent business, and the timing of the discount doesn't really matter, then you shouldn't use Prophet to predict the amount of sales. In that case, you're dealing with a regression problem and I'd recommend something else (RF, etc.).
But if you have a time-dependency on the amount of sales due to varying discounts (e.g. Black Friday) Prophet would be able to predict the amount of sales. See the tutorial (Sales equals #hits on Wikipedia): https://facebook.github.io/prophet/docs/quick_start.html
I'd recommend first to evaluate the predictability of the amount of sales dependent on the discount with classic regression. The time-part (Prophet) comes later.
This would be possible, although I have my reservations. If you have a time-independent business, and the timing of the discount doesn't really matter, then you shouldn't use Prophet to predict the amount of sales. In that case, you're dealing with a regression problem and I'd recommend something else (RF, etc.).
But if you have a time-dependency on the amount of sales due to varying discounts (e.g. Black Friday) Prophet would be able to predict the amount of sales. See the tutorial (Sales equals #hits on Wikipedia): https://facebook.github.io/prophet/docs/quick_start.html
I'd recommend first to evaluate the predictability of the amount of sales dependent on the discount with classic regression. The time-part (Prophet) comes later.
Hey,
Since all retails would have discounts during the whole year, so I want to focus on that. This is why I need datetime, month and day(Monday, Tuesday) included so I can determine the optimal sales.
Do you mean use RF first to look whether discount impacts amount of sales, and then look at other time based variable?
Do you mean use RF first to look whether discount impacts amount of sales, and then look at other time based variable?
Yes, that's what I meant.
@FancyPandaSoftworks Check out Stack Overflow, like https://stats.stackexchange.com/questions/313851/time-series-forecasting-in-r
There's several questions that do exactly what you're asking. If you have questions, try to find an R mentor or post on Stack Overflows.
So recently I am experimenting with Prophet, a time-series analysis.
My data is transaction data, so you have the days, date, product name, product type, the price, original price and discount.
I am able to predict the sales of the data, but now I am wondering whether it is possible to determine the sales of the product when I let's say, put the discount value for the product to 20% only for the next following month.
Multiple discount values are used over the course of years, and now I want to test with the discount value to determine the best discount value for next month.
Since the dataset for Prophet is y,ds I am not sure whether I can use Prophet for these kinds of analysis.
This is (a part of) my current code to create a prophet plot:
Any suggestions how the model can predict based on (for this time:) the given discount value?