NeuralProphet - Quick Start¶
Installing¶
NeuralProphet can be installed with pip:
$ pip install neuralprophet
If you plan to use the package in a Jupyter notebook, we recommend to install the ‘live’ version:
$ pip install neuralprophet[live]
Alternatively, you can get the most up to date version by cloning directly from GitHub:
$ git clone https://github.com/ourownstory/neural_prophet.git
$ cd neural_prophet
$ pip install .
Simple Model¶
The input data should have two columns, ds
which has the timestamps and y
column which
contains the observed values of the time series.
from neuralprophet import NeuralProphet
import pandas as pd
data_location = "https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/"
df = pd.read_csv(data_location + 'wp_log_peyton_manning.csv')
To setup a simple NeuralProphet model, create an object of the NeuralProphet
class as follows and call the fit function. Frequency is automatically detected 😉
m = NeuralProphet()
metrics = m.fit(df)
Now we can simple make predictions using this fitted model by creating a future dataframe and setting the forecast horizon via the functional argument periods
:
future = m.make_future_dataframe(df=df, periods=365)
forecast = m.predict(df=future)
Next, we obtain the forecast and visualize our data… et voila 👩🏼🎨
fig_forecast = m.plot(forecast)

Note
Congrats! 🥳 You successfully trained your first NeuralProphet model! Checkout the Full Simple Model description for additional basic features of NeuralProphet. 🚀
Get started with Tutorials¶
Feature Tutorials
Application Tutorials