NeuralProphet Forcasting

NeuralProphet.py

# install neuralprophet
!pip install neuralprophet

# import libraries 
import pandas as pd
import matplotlib.pyplot as plt
import plotly.offline as py
import matplotlib.pyplot as plt
%matplotlib inline
#  import neuralprophet
from neuralprophet import NeuralProphet

# read data
df1 = pd.read_csv("stock.csv")
df1.head()

# select Date and Adj Close from the dataset
df2 = df1[['Date','Adj Close']]
df2.tail()

# change names to fit NeuralProphet req 
apple_features = df2.rename(columns = {"Date":"ds","Adj Close":"y"})
apple_features.head()

# model and fit D= clander days freq
model = NeuralProphet()
metrics = model.fit(apple_features, 
                    freq='D', epochs=1000)

#predict
forecast = model.predict(apple_features)

# plot
fig_forecast = model.plot(forecast)
fig_components = model.plot_components(forecast)
fig_model = model.plot_parameters()