Prophet manual

It's made to let you collaborate with your friends and coworkers without needing any kind of special server or Internet provider. One way is to clone an existing database. When you clone an existing database, a local replica is created for you with the uuid of the cloned database. Another way is to init a database. This creates a database with a new uuid. Anyone who clones from this database will get a replica of the data and share the database uuid.

Note that Prophet will prevent you from merging databases unless they have the same database uuid although you can force the merge of different databases if you want. A Prophet database is composed of records, each of which has several properties. Two core properties are type and uuid. A record's type indicates the kind of record comment, ticket, user, etc. Another core property is a record's luid , which is a shorthand identifier used for local identification.

For example:. A record object in Prophet is initially an empty husk. First, the record class is found. The default record class is Prophet::Record , but designating a custom class, one that extends from Prophet::Record is possible.

The record is now ready for use. Once you have a record object configured with a type and uuid, you can load data from the replica. The y column must be numeric, and represents the measurement we wish to forecast. We scraped this data using the Wikipediatrend package in R. The CSV is available here. 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 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. By default it will also include the dates from the history, so we will see the model fit as well.

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.

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