Questions tagged [prophet]

An automatic forecasting system developed by Facebook. Use this tag for any on-topic question that (a) involves Prophet either as a critical part of the question or expected answer and (b) is not just about how to use Prophet.

Filter by
Sorted by
Tagged with
0 votes
0 answers
14 views

How can I do a comparative analysis of fbprophet time-series model results in pyspark? [closed]

I am using fbprophet for time-series forecasting on an unique ID of a big data with thousands of unique ...
user avatar
  • 101
0 votes
1 answer
22 views

Information criteria to select best Prophet model

How do you optimize your hyperparameters when using prophet for forecasting? I have been using cross validation and I don't know why no information criteria (such as AIC or BIC) has been implemented ...
user avatar
  • 11
0 votes
1 answer
39 views

Prophet trend model

Can someone help me understand how Prophet automatically detects the change points in its piecewise linear trend model? Please check page 10 of Prophet paper (Forecasting at Scale: https://peerj.com/...
user avatar
  • 11
1 vote
1 answer
66 views

Aggregation of Interval Predictions

Given a montly time series, my objective is to provide my client with the next 12 point forecasts along with a yearly forecast. To obtain the yearly forecast, I simply sum up the 12 points forecasts. ...
user avatar
  • 11
0 votes
0 answers
9 views

Comparing a control model forecast with the actual results for significant difference

I'm looking to compare a control model which models the performance of a business based on many similar business. During a period this business had a renovation and I'm using the other businesses to ...
user avatar
  • 1
0 votes
0 answers
29 views

Confidence interval for summation of time series

I have three time-series of denoting forecast means with confidence intervals around them. I want to add these three models and get a confidence interval for the final model. How should I approach ...
user avatar
1 vote
0 answers
32 views

Prophet not fitting data correctly

I'm currently looking to build a time-series model to do change point detection on a churn dataset, in order to check that the covid19 measures taken during the pandemic had an influence on churn. I'm ...
user avatar
0 votes
0 answers
11 views

Does Prophet give any indication of the quality of fit without needing to predict the training data again?

I was wondering wheter a FB Prophet model gives any indication of the quality of the model fit, either as an aggregated statistic, like a mean absolute error, or as a vector with residuals per ...
user avatar
  • 113
5 votes
1 answer
1k views

Facebook prophet gives a very high MAPE, how can I improve it?

I have some daily sales from 2018-01-01 to 2021-10-21 and I'm trying to predict the sales a year into the future. I opted for facebook prophet. My raw data looks like this: According to a DF-test, ...
user avatar
  • 329
13 votes
3 answers
934 views

Why is non-iid noise so important to traditional time-series approaches?

I've been reading the whitepaper that accompanied Facebook's release of Prophet, it's time-series modeling library. One topic the authors drew attention to was that noise was assumed to be iid; they ...
user avatar
  • 1,650
0 votes
0 answers
11 views

Using historical proportions for prediction intervals estimated at a more aggregated level?

Currently, I am working on quite a general problem where I need to forecast some business-related value like target events (clicks, acquisitions, etc). Given the number of different breakdowns (by ...
user avatar
1 vote
0 answers
55 views

cross validation and hyperparameter tuning for multiple time series

I have time series data for 2000 products. If I use models like fbprophet or SARIMAX or xgboost then the cross validation needs to be done for 2000 time series data. for a single time series data it ...
user avatar
0 votes
0 answers
39 views

Is a Laplace Prior the same thing or related to a Laplace Transformation?

Context: I was watching this video https://youtu.be/pOYAXv15r3A?t=796 about Facebook Prophet and the speaker mentioned they use a Laplace Prior $$\delta \sim Laplace(\lambda)$$. What I have gleaned so ...
user avatar
0 votes
0 answers
56 views

Forecasting a distribution

My current struggle is to forecast the distribution of a given value across the next 360 days. e.g. The context is hotel accomodation bookings. We have a forecast of 100 bookings to be made for time t ...
user avatar
0 votes
0 answers
49 views

Selecting values for time series cross-validation (Facebook Prophet)

Let's say I have a problem with ~400 daily obervations and I need to forecast 7 days ahead. The goal is to report how accurate such forecasts can be. Facebook Prophet provides a convenient method <...
user avatar
0 votes
1 answer
70 views

Forecasting problem - Fewer data points and covid impact

I have quarterly data from 2017 to 2020 (16 data points) to forecast. I tried to use SARIMA but it is giving me weird numbers (High and negative fitted values). Also, I tried exponential smoothening ...
user avatar
2 votes
0 answers
48 views

How to transform a daily average temperatures forecast into an hourly forecast based on the hourly temperature profile observed historically?

I need to transform a daily average temperatures forecast into an hourly forecast based on the hourly temperature profile observed historically. I work in Python. I have found ways of forecasting the ...
user avatar
0 votes
0 answers
10 views

Components plots is inconsistent with df_pred on monthly seasonality

Background I do seasonality decomposation with timeseries. In my use case, I have trend/weekly(7 days)/monthly(30.5 days as prophet doc suggests) components. For me, I want to get the data of each ...
user avatar
  • 217
2 votes
0 answers
253 views

What is the best approach to grouped time-series forecasting?

Let's say we have data on the number of clicks per user over quite a long period of time. We can use, say Facebook Prophet, to forecast daily values given that we have enough historical data. That ...
user avatar
0 votes
0 answers
125 views

Unsupervised anomaly detection on multivariate time series

Me and my team face the next use case: Our data consists of 3 numerical signals, Which are collected every 10 minutes. Example: Our main goal is to build an anomaly detection algorithm. Our work till ...
user avatar
2 votes
1 answer
80 views

Adjustment factor in logistic growth model of facebook-prophet

In the paper it emphasizes: "When the rate k is adjusted, the offset parameter m must also be adjusted to connect the endpoints of the segments. The correct adjustment at changepoint j is easily ...
user avatar
1 vote
0 answers
434 views

Why does prophet produce much tighter prediction intervals than ETS?

I'm currently working on a forecast problem, where narrow prediction intervals are preferred. When I look at the prediction intervals of ETS and prophet forecasts, I'm surprised that the prophet ...
user avatar
  • 267
1 vote
1 answer
642 views

What could cause facebook's Prophet model to do so poorly on these procedurally generated datasets, where one is a continuation of the other [closed]

Recently I've been looking into some easy out of the box modeling using Facebook's Prophet -- potentially to use in some projects at work. So far, I have been super impressed with everything that I've ...
user avatar
  • 133
1 vote
1 answer
387 views

Can you combine Facebook Prophet (fbprophet) with ARMA?

I am new to dealing with time-series data, so my apologies if this is not a valid question. I am wondering if there is a way to combine ARMA with fbprophet. From what I understand, fbprophet can take ...
user avatar
2 votes
0 answers
366 views

What are the underlying statistical differences between the vector autoregression model and Prophet?

I am trying to understand the underlying fundamental/statistical differences between vector autoregression models and Facebook's Prophet, with regards to multivariate time series forecasting. I am ...
user avatar
0 votes
0 answers
54 views

How to predict unknown time series in using Facebook Prophet?

The problem is predicting hits on different stories published on a website. I am aware that for this kind of time series forecasting, facebook prophet is a popular library. However, it seems in ...
user avatar
  • 453
2 votes
1 answer
1k views

Why do time series, with positive values decomposed into seasonal plots, have negative values

I have been running different forecasting algorithms such as Facebook Prophet and Forecast (from R) and I note that despite all my time series values being positive, my seasonality values are negative....
user avatar
6 votes
2 answers
3k views

Inference in Time Series: Prophet vs. ARIMA

I read through Prophet's white paper and they mention that their algorithm, "gives up some important inferential advantages of a generative model such as an ARIMA." (page 7) So now I'm ...
user avatar
2 votes
1 answer
152 views

Is summing daily forecasts a sound method for generating weekly/monthly forecasts?

I'm new to time series analysis, and I am wondering if this is a sound method for generating weekly and monthly predictions. In my case, I need to generate daily, weekly, and monthly predictions. If ...
user avatar
  • 21
0 votes
1 answer
72 views

logistic growth and covid cases/deaths

A few blogs suggest that at least the growth of covid cases (and deaths?) follows a logistic groth curve. See: https://medium.com/katanaml/covid-19-growth-modeling-and-forecasting-with-prophet-...
user avatar
  • 1,306
1 vote
1 answer
4k views

Why yhat fbprophet returns with negative prediction

I'm trying to forecast with fbprophet, the input are all positive but the predictions returns negative i'm kind of confused, i read this quick start and if the inputs are all positive then the ...
user avatar
0 votes
1 answer
152 views

prophet cross validation

I'm confused by the 'cross_validation' of prophet. In the following cross validation process, were parameters learned and saved to the model? is this cross validation used to train model or just to ...
user avatar
1 vote
0 answers
46 views

how to calculate the confidence interval when sum the predictions

I want to predict the total sales of 10 stores. 8 of the 10 stores have 10 years sales data available, while the other 2 stores only have 2 and 4 years sales data. Firstly, I use prophet to build one ...
user avatar
0 votes
0 answers
80 views

R - How to create a forecast object from a pre-defined forecast, in order to apply accuracy() for MASE result

I am working with a forecast which has been created by Prophet. I would like to apply accuracy() to return the MASE after identifying this as the best accuracy measure for multiple forecasts, and will ...
user avatar
1 vote
0 answers
230 views

why prophet doesn't capture some holiday effect well [closed]

I used prophet for time series forecasting. the holiday effect for Christmas and thanksgiving were captured well. The data show strong positive effect for the Easter Sunday('lower_window': -2, '...
user avatar
1 vote
1 answer
139 views

prophet holiday effect

I use prophet to do time series forecasting. During Christmas season, the data rise from 12/22, reach peak on 12/23 and 12/24 (positive effect), then drop a lot on 12/25 and 12/26 (negative effect). ...
user avatar
0 votes
0 answers
453 views

I am trying to build a monthly revenue prediction model using Prophet in Python but how can I optimize the hyperparameters of the model?

The hyperparameters which I am trying to optimize are: 'n_changepoints', 'changepoint_range', 'holidays_prior_scale', 'seasonality_prior_scale' and 'changepoint_prior_scale'. 'changepoint_range' has a ...
user avatar
2 votes
0 answers
93 views

How do additional regressors and seasonality get incorporated into fbprophet? [closed]

I've been playing around with fbprophet here and I've noticed they specify their base additive model is $$y(t) = g(t) + s(t) + h(t) + \epsilon_t$$ My question is two-fold: 1) How does modifying ...
user avatar
  • 23
2 votes
0 answers
906 views

Dealing with non continuous data in time series

My current project involves building forecasting model for asset - desktop, laptop and monitors. The data is for the last 3 years. There is good amount of seasonality in the data. There are few data ...
user avatar
0 votes
1 answer
65 views

Data aggregation with facebook prophet

I'm using facebook's prophet library for forecast, the library seems to work very well but I'm not finding in the documentation how to handle multiple values for the same period, E.G: I'm working ...
user avatar
3 votes
1 answer
218 views

Standardise components of an additive model output

I've got a sales forecasting model using the fbprophet library. The model is additive: calculates a base trend and then adds ...
user avatar
  • 131
2 votes
1 answer
338 views

Estimate the time series like an event was never happened

I have data from a website where a specific advertising campaign happened a couple of years ago. What I want to do is to estimate how the signups on that website would have been without that big ...
user avatar
  • 93
2 votes
3 answers
1k views

Forecasting daily data with zeros in Python

I'm currently testing some forecasts on daily sales quantities. However, out of ~2000 observations I have 16 zeros. How should I approach this? It's mainly Sundays and holidays that holds zero as ...
user avatar
  • 155
1 vote
1 answer
724 views

Forecasting recurring orders for an online subscription business using Facebook Prophet and R

I am analyzing data from a subscription model, in which a customer must pay a recurring price at a regular interval (30 days) for access to the product. EDIT -> Direct link to daily data: https://...
user avatar
15 votes
4 answers
5k views

Is Prophet from Facebook any different from a linear regression?

So what I've read about Facebook's prophet is that it basically breaks down the time series into trend and seasonality. For example, an additive model would be written as: $$ y(t) = g(t) + s(t) + h(t)...
user avatar
  • 263
8 votes
1 answer
5k views

How does facebook prophet handle missing data?

The Prophet paper (forecasting at scale by SJ Taylor - 2017) says the following on missing data: Unlike ARIMA models, the measurements do not need to be regularly spaced, and we do not need to ...
user avatar