AI News, Open Machine Learning Course. Topic 9. Part 2. Predicting the future with Facebook Prophet

Open Machine Learning Course. Topic 9. Part 2. Predicting the future with Facebook Prophet

These are only some of the conceivable predictions of future trends that might be useful: For another example, we can make a prediction of some team’s performance and then use it as a baseline: first to set goals for the team, and then to measure the actual team performance relative to the baseline.

This library tries to address the following difficulties common to many business time series: The authors claim that, even with default settings, in many cases, their library produces forecasts as accurate as those delivered by experienced analysts.

What is especially important, these parameters are quite comprehensible even for non-experts in time series analysis, which is a field of data science requiring certain skill and experience.

By the way, the original article is called “Forecasting at Scale”, but it is not about the scale in the “usual” sense, that it’s addressing computational and infrastructure problems of a large number of working programs.

It is represented in the form of the logistic growth model: where: This logistic equation allows modelling non-linear growth with saturation, that is when the growth rate of a value decreases with its growth.

Six new variables are added: monday, tuesday, wednesday, thursday, friday, saturday, which take values 0 or 1 depending on the day of the week.

Along the way we get rid of possible duplicates and missing values in the data: Next, we need to convert published to the datetime format because by default pandas treats this field as string-valued.

We will just trim our time series to keep only those rows that fall onto the period from August 15, 2012 to June 25, 2017: As we are going to predict the number of published posts, we will aggregate and count unique posts at each given point in time.

First, we import and initialize the Plotly library, which allows creating beautiful interactive plots: We also define a helper function, which will plot our dataframes throughout the article: Let’s try and plot our dataset as is: High-frequency data can be rather difficult to analyze.

We save our downsampled dataframe in a separate variable because further in this practice we will work only with daily series: Finally, we plot the result: This downsampled chart proves to be somewhat better for an analyst’s perception.

One of the most useful functions that Plotly provides is the ability to quickly dive into different periods of timeline in order to better understand the data and find visual clues about possbile trends, periodic and irregular effects.

Second, these first years, having very low number of posts per day, are likely to increase noise in our predictions, as the model would be forced to fit this abnormal historical data along with more relevant and indicative data from the recent years.

The input to the method fit is a DataFrame with two columns: To get started, we’ll import the library and mute unimportant diagnostic messages: Let’s convert our dataframe to the format required by Prophet: The authors of the library generally advise to make predictions based on at least several months, ideally, more than a year of historical data.

To measure the quality of our forecast, we need to split our dataset into the historical part, which is the first and biggest slice of our data, and the prediction part, which will be located at the end of the timeline.

Then we train our model by invoking its fit method on our training dataset: Using the helper method Prophet.make_future_dataframe, we create a dataframe which will contain all dates from the history and also extend into the future for those 30 days that we left out before.

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