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

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

#predict
forecast = model.predict(apple_features)

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# 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"})

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# 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

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import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import train_test_split

# Reading and converting Date
data = pd.read_csv('AAPL.csv')
data['Date'] = pd.to_datetime(data['Date'], infer_datetime_format=True)
data.info()

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### Create the Stacked LSTM model
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Dropout
from tensorflow.keras.layers import LSTM
from tensorflow.keras.initializers import RandomNormal, Constant
import tensorflow as tf
# Set random seed for reproducibility: get the same result after each time running the model
tf.random.set_seed(1234)

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### with spliting train and test   
m = NeuralProphet() 
df_train, df_test = m.split_df(apple_features, valid_p=0.3,freq='D')

# fiting 
metricss = m.fit(df_train, validation_df=df_test , freq='D',epochs=1000)

# predict
forecast = model.predict(df_train)

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# 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

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### Create the Bidirectional LSTM model
from random import random
from numpy import array
from numpy import cumsum
from keras.models import Sequential
from tensorflow.keras.layers import Dense,Dropout
from keras.layers import TimeDistributed
from keras.layers import Bidirectional
from tensorflow.keras.layers import LSTM

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import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn.model_selection import train_test_split

# Reading and converting Date
data = pd.read_csv('AAPL.csv')
data['Date'] = pd.to_datetime(data['Date'], infer_datetime_format=True)
data.info()