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.

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Trend in exogenous variable in time series

I have a time series of a variable V1 with seasonality and a strong trend. The trend however seems to be closely related to (and caused by) the trend of another time varying variable (V2). As V2 grows ...
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1 answer
42 views

How to predict Long term time series?

I am working on a time series problem where I am trying to predict the temperature of a product in a refrigerator by using the information about the temperature of the air in the refrigerator with ...
2 votes
2 answers
334 views

Analysis of forecast errors from Facebook Prophet

I created a forecasting model Facebook Prophet and now trying to analyse the forecast errors (yhat - forecasted). Following are 3 graphs I plotted First one is raw forecast errors, second one is ...
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Different Series For Forecasting

I have a time series data for different programs and each program has its own sequence for applications.For example , program 2020/03 there are applicants who applied in year 2015,2016,2017 till the ...
-1 votes
1 answer
84 views

estimation of a yearly prediction interval for monthly data

I have a monthly time series and 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 summed up the 12 points ...
3 votes
2 answers
86 views

Using weather forecasts as exogenous data for timeseries forecasting

I'm using Facebook Prophet as a forecasting model and I want to use weather data (temp for example) as additional regressor (exogenous data or external variable). Additional regressors are integrated ...
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1 answer
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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 ...
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1 answer
111 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/...
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18 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 ...
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1 vote
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121 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 ...
1 vote
0 answers
88 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 ...
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0 answers
36 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 ...
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5 votes
1 answer
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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, ...
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13 votes
3 answers
1k 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 ...
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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 ...
1 vote
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126 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 ...
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50 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 ...
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63 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 ...
1 vote
0 answers
108 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 <...
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1 answer
131 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 ...
2 votes
0 answers
56 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 ...
2 votes
0 answers
505 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 ...
0 votes
0 answers
162 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 ...
2 votes
1 answer
95 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 ...
1 vote
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561 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 ...
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1 vote
1 answer
990 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 ...
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1 vote
1 answer
495 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 ...
2 votes
1 answer
501 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 ...
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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 ...
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2 votes
1 answer
2k 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....
6 votes
2 answers
4k 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 ...
2 votes
1 answer
181 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 ...
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1 answer
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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-...
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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 ...
0 votes
1 answer
236 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 ...
1 vote
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60 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 ...
0 votes
0 answers
100 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 ...
1 vote
0 answers
315 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, '...
1 vote
1 answer
190 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). ...
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0 answers
497 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 ...
3 votes
0 answers
112 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 ...
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2 votes
0 answers
1k 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 ...
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1 answer
81 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 ...
3 votes
1 answer
235 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 ...
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2 votes
1 answer
351 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 ...
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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 ...
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1 vote
1 answer
757 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://...
15 votes
4 answers
6k 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)...
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10 votes
1 answer
6k 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 ...