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п»їQuick Start.
Python API.
Prophet follows the sklearn model API. We create an instance of the Prophet class and then call its fit and predict methods.
The input to Prophet is always a dataframe with two columns: ds and y . The ds (datestamp) column should be of a format expected by Pandas, ideally YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp. The y column must be numeric, and represents the measurement we wish to forecast.
As an example, let’s look at a time series of the log daily page views for the Wikipedia page for Peyton Manning. We scraped this data using the Wikipediatrend package in R. Peyton Manning provides a nice example because it illustrates some of Prophet’s features, like multiple seasonality, changing growth rates, and the ability to model special days (such as Manning’s playoff and superbowl appearances). The CSV is available here.
First we’ll import the data:
ds y 0 2007-12-10 9.590761 1 2007-12-11 8.519590 2 2007-12-12 8.183677 3 2007-12-13 8.072467 4 2007-12-14 7.893572.
We fit the model by instantiating a new Prophet object. Any settings to the forecasting procedure are passed into the constructor. Then you call its fit method and pass in the historical dataframe. Fitting should take 1-5 seconds.
Predictions are then made on a dataframe with a column ds containing the dates for which a prediction is to be made. You can get a suitable dataframe that extends into the future a specified number of days using the helper method Prophet.make_future_dataframe . By default it will also include the dates from the history, so we will see the model fit as well.
ds 3265 2017-01-15 3266 2017-01-16 3267 2017-01-17 3268 2017-01-18 3269 2017-01-19.
The predict method will assign each row in future a predicted value which it names yhat . If you pass in historical dates, it will provide an in-sample fit. The forecast object here is a new dataframe that includes a column yhat with the forecast, as well as columns for components and uncertainty intervals.
ds yhat yhat_lower yhat_upper 3265 2017-01-15 8.204125 7.449654 8.946255 3266 2017-01-16 8.529148 7.792752 9.284594 3267 2017-01-17 8.316555 7.563541 9.029357 3268 2017-01-18 8.149153 7.384345 8.840279 3269 2017-01-19 8.161075 7.430352 8.859482.
You can plot the forecast by calling the Prophet.plot method and passing in your forecast dataframe.
If you want to see the forecast components, you can use the Prophet.plot_components method. By default you’ll see the trend, yearly seasonality, and weekly seasonality of the time series. If you include holidays, you’ll see those here, too.
An interactive figure of the forecast and components can be created with plotly. You will need to install plotly 4.0 or above separately, as it will not by default be installed with fbprophet. You will also need to install the notebook and ipywidgets packages.
More details about the options available for each method are available in the docstrings, for example, via help(Prophet) or help(Prophet.fit) . The R reference manual on CRAN provides a concise list of all of the available functions, each of which has a Python equivalent.
R API.
In R, we use the normal model fitting API. We provide a prophet function that performs fitting and returns a model object. You can then call predict and plot on this model object.
First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns ds and y , containing the date and numeric value respectively. The ds column should be YYYY-MM-DD for a date, or YYYY-MM-DD HH:MM:SS for a timestamp. As above, we use here the log number of views to Peyton Manning’s Wikipedia page, available here.
We call the prophet function to fit the model. The first argument is the historical dataframe. Additional arguments control how Prophet fits the data and are described in later pages of this documentation.
Predictions are made on a dataframe with a column ds containing the dates for which predictions are to be made. The make_future_dataframe function takes the model object and a number of periods to forecast and produces a suitable dataframe. By default it will also include the historical dates so we can evaluate in-sample fit.
As with most modeling procedures in R, we use the generic predict function to get our forecast. The forecast object is a dataframe with a column yhat containing the forecast. It has additional columns for uncertainty intervals and seasonal components.
You can use the generic plot function to plot the forecast, by passing in the model and the forecast dataframe.
You can use the prophet_plot_components function to see the forecast broken down into trend, weekly seasonality, and yearly seasonality.


Correct Score: Prophet Mbonye prediction on US politics comes to life as Joe Biden selects influencial Harris Kamala.
By Reporter.
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Prophet Elvis Mbonye continues to surprise doubting Thomas with his prophesies. At the start of this year on 7th Janury before heated politics in the US, prophet Mbonye prophesied an emerging female figure in the Democratic Party in the US something far-fetched considering even the Biden Joe who appointed a female Challenger was still lagging. As the elections in November grew nearer DP has a very influencial female figure Kamala Harris.
Watch his prophesy in January.
Prophet Mbonye further elaborated in his prophecy that the female figure will have wielding influence behind the Democratic Party, and true to that, already some political commentators like Turker Colson predict that she will be the one running the country.
In 2013, Prophet Elvis Mbonye prophesized about Joe Biden’s health which has since come to pass too.


Top 10 Most Accurate Prophets in History.
Prophecy is a very dangerous and tricky activity for any person to perform. The reason why prophecy is so dangerous is that people can use this form of religious or spiritual practice to deceive individuals. Throughout the millennia there have been thousands of prophets.
Some of these individuals were major prophets while others where minor seers. Most prophets do prove to be false. However, some individuals who provided prophetic predictions were (or still are) very accurate. The following list will describe the top 10 most accurate prophets in history and their important predictions that have come true.
10. Jeremiah.
Jeremiah the prophet was used by God of the Jewish and Christian people at some time between 628 BC to 586 BC. His message was to the Jewish people and what was going to happen to them at the hand of the Babylonians. God was punishing the Jews for their disobedience and he used the Babylonian people to carry out his discipline.
Jeremiah told the Jews that they would be taken into captivity for 70 years by the Babylonians. He also told them that the Messiah (Jesus Christ) would be a descendant of King David. All of Jeremiah’s predictions came true even though the people of his time ridiculed and dismissed him.
9. John the Baptist.
John the Baptist was the last prophet to speak out about Jesus Christ before he arrived on the scene in Jerusalem. John was born shortly before Jesus and that would have been sometime between 10 B.C. and 0 B.C. The Old Testament prophet Isaiah spoke of John’s coming shortly before Jesus’s arrival. Once John the Baptist was preaching about Christ, he started to make predictions about the coming Messiah. John was actually Jesus’s cousin. The predictions that he made about the Messiah were all true and they all came to pass.
8. Abraham.
Abraham is a Jewish patriarch that lived close to 4000 years ago in the Middle East. He was the first person that God used to start the Jewish nation. During his lifetime, Abraham had many adventures with God and he also made many prophecies.
Abraham revealed that God would be the father of many nations, that his descendants would have their own nation, that the Jewish people would be a great race in the world and that the Messiah (Jesus Christ) would be one of his future descendants. All of these predictions came true thousands of years after Abraham had passed.
7. The Brahan Seer.
The Brahan Seer was a Scottish prophet who served the Earl of Seaforth during the 19th century. The Brahan Seer’s prophecies were more about the fate of the Scottish people – especially those in power. He made predictions about how certain noblemen such as the MacKenzies of Fairburn will lose their wealth and possessions.
He also said that certain monuments and landmarks within the kingdom of Scotland would be destroyed. He also told his people that when the fived bridges over the River Ness in Inverness would be destroyed. Once this happened world chaos was supposed to have followed.
Unfortunately, this prediction came true when Hitler invaded Poland during WW II. Brahan Seer lost his life when he predicted that the Earl of Seaforth was cheating on his wife. Apparently, the Earl’s wife had him killed for stating such things – even though they were true.
6. Joan Quigley.
Joan Quigley was an unusual prophet or seer because she was used by President Reagan and his First Lady Nancy. Normally, most presidents would not consult a psychic or medium before they engaged in political affairs. However, the Reagans did. They would often consult Quigley before they would meet other political leaders or before they made some type of major political decision.
Eventually the public found out that the Reagans were using a medium to help determine political affairs. However, this incident was quickly and quietly swept under the rug. No American really wants a president who consults mediums and psychics to figure out political affairs.
5. Edgar Cayce.
Edgar Cayce was a popular seer that lived during the early 20th century. He would invite people to his home and have them ask questions about their future. While they asked the questions, Cayce would lie down and enter a trance like state. He would then answer them.
Cayce made thousands of predictions. He supposedly accurately predicted Hitler’s activities and the Great Depression. He also predicted that California would fall into the ocean. However, as we already know, this did not happen. Not all of Cayce’s predictions came true.
4. Madam Marie.
Madam Marie was an American seer that performed this activity for well over 70 years. She made her predictions in the New Jersey area of Asbury Park Boardwalk. One of her most notable predictions was that Bruce Springsteen would become a music star.
This prediction did come to pass. Springsteen referenced the Madam Marie in his song 4th of July and he even helped her to gain a following. Other notable stars such as Ray Charles, Woody Allen and Elton John used her services as well.
3. Noah.
Noah is the famous Jewish Patriarch that lived about 10,000 years ago. He built the great ark which as used to withstand the flood waters that God sent to destroy the world. Many people might be familiar with Noah and the ark but what they probably did not know is that Noah preached for over 100 years while building the ark.
He was trying to get people to turn from their sins and follow God. While he was preaching, he prophesized that the world would be destroyed by water and this came to pass. He also prophesized that people would be destroyed if they did not repent. He was right.
2. Nostradamus.
Nostradamus was an unusual prophet because he could accurately predict a lot of things and they really would happen. Nostradamus never claimed to be a prophet and he often stated that his predictions might not happen at all.
However, this seer did not have all of his predictions to come true. He predicted some things about WW II that actually happened. Yet, other of his prophecies have yet to manifest. Nostrodamus still remains one of the most influential prophets in modern time even though he died in 1566.
1. Jesus.
Jesus Christ is the most controversial figure in all of humanity. One of things that makes him such an enigma for many people is his bold prophecies. Jesus predicted that the temple in Jerusalem would be destroyed and it happened. He also predicted that he would die and rise again to become the savior of humanity.
This happened as well. Jesus made predictions about the start and spread of Christianity and he made prophecies concerning the end times. Christ is also the Messiah and many Old Testament prophets made many prophecies about him in the past. Christ is a prophetic making machine and all of his prophecies have come to pass or will happen in the future.
Keep in mind that all people who make prophecies should be taken with caution. All of the people on this list made accurate predictions but keep in mind that they are the exception and not the rule.


Time Series Forecasting With Prophet in Python.
Time series forecasting can be challenging as there are many different methods you could use and many different hyperparameters for each method.
The Prophet library is an open-source library designed for making forecasts for univariate time series datasets. It is easy to use and designed to automatically find a good set of hyperparameters for the model in an effort to make skillful forecasts for data with trends and seasonal structure by default.
In this tutorial, you will discover how to use the Facebook Prophet library for time series forecasting.
After completing this tutorial, you will know:
Prophet is an open-source library developed by Facebook and designed for automatic forecasting of univariate time series data. How to fit Prophet models and use them to make in-sample and out-of-sample forecasts. How to evaluate a Prophet model on a hold-out dataset.
Let’s get started.
Time Series Forecasting With Prophet in Python Photo by Rinaldo Wurglitsch, some rights reserved.
Tutorial Overview.
This tutorial is divided into three parts; they are:
Prophet Forecasting Library Car Sales Dataset Load and Summarize Dataset Load and Plot Dataset Forecast Car Sales With Prophet Fit Prophet Model Make an In-Sample Forecast Make an Out-of-Sample Forecast Manually Evaluate Forecast Model.
Prophet Forecasting Library.
Prophet, or “ Facebook Prophet ,” is an open-source library for univariate (one variable) time series forecasting developed by Facebook.
Prophet implements what they refer to as an additive time series forecasting model, and the implementation supports trends, seasonality, and holidays.
Implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.
It is designed to be easy and completely automatic, e.g. point it at a time series and get a forecast. As such, it is intended for internal company use, such as forecasting sales, capacity, etc.
For a great overview of Prophet and its capabilities, see the post:
The library provides two interfaces, including R and Python. We will focus on the Python interface in this tutorial.
The first step is to install the Prophet library using Pip, as follows:
Next, we can confirm that the library was installed correctly.
To do this, we can import the library and print the version number in Python. The complete example is listed below.
Running the example prints the installed version of Prophet.
You should have the same version or higher.
Now that we have Prophet installed, let’s select a dataset we can use to explore using the library.
Car Sales Dataset.
We will use the monthly car sales dataset.
It is a standard univariate time series dataset that contains both a trend and seasonality. The dataset has 108 months of data and a naive persistence forecast can achieve a mean absolute error of about 3,235 sales, providing a lower error limit.
No need to download the dataset as we will download it automatically as part of each example.
Load and Summarize Dataset.
First, let’s load and summarize the dataset.
Prophet requires data to be in Pandas DataFrames. Therefore, we will load and summarize the data using Pandas.
We can load the data directly from the URL by calling the read_csv() Pandas function, then summarize the shape (number of rows and columns) of the data and view the first few rows of data.
The complete example is listed below.
Running the example first reports the number of rows and columns, then lists the first five rows of data.
We can see that as we expected, there are 108 months worth of data and two columns. The first column is the date and the second is the number of sales.
Note that the first column in the output is a row index and is not a part of the dataset, just a helpful tool that Pandas uses to order rows.
Load and Plot Dataset.
A time-series dataset does not make sense to us until we plot it.
Plotting a time series helps us actually see if there is a trend, a seasonal cycle, outliers, and more. It gives us a feel for the data.
We can plot the data easily in Pandas by calling the plot() function on the DataFrame.
The complete example is listed below.
Running the example creates a plot of the time series.
We can clearly see the trend in sales over time and a monthly seasonal pattern to the sales. These are patterns we expect the forecast model to take into account.
Line Plot of Car Sales Dataset.
Now that we are familiar with the dataset, let’s explore how we can use the Prophet library to make forecasts.
Forecast Car Sales With Prophet.
In this section, we will explore using the Prophet to forecast the car sales dataset.
Let’s start by fitting a model on the dataset.
Fit Prophet Model.
To use Prophet for forecasting, first, a Prophet() object is defined and configured, then it is fit on the dataset by calling the fit() function and passing the data.
The Prophet() object takes arguments to configure the type of model you want, such as the type of growth, the type of seasonality, and more. By default, the model will work hard to figure out almost everything automatically.
The fit() function takes a DataFrame of time series data. The DataFrame must have a specific format. The first column must have the name ‘ ds ‘ and contain the date-times. The second column must have the name ‘ y ‘ and contain the observations.
This means we change the column names in the dataset. It also requires that the first column be converted to date-time objects, if they are not already (e.g. this can be down as part of loading the dataset with the right arguments to read_csv ).
For example, we can modify our loaded car sales dataset to have this expected structure, as follows:




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