Project At A Glance

Objective: Forecast and extrapolate prices for the Apple Stock (AAPL) over time using Time-Series data from the last five years.

Data: AAPL Stock Dataset from Tiingo API [Download]

Implementation: Time-Series Forecasting, Stacked Long Short-Term Memory (LSTM), Scaling and Transforms

Results:

  • Visualized forecasting for the stipulated period of 5 years.
  • Extended values from the 100 most recent days to predict the next 30 days.
  • The model projected a mild plateau in future valuation on the time of project instantiation.

Deployment: View this project on GitHub.

Dataset

Fetching 

import pandas_datareader as pdr
key=""

df = pdr.get_data_tiingo('AAPL', api_key=key)

df.to_csv('AAPL.csv')

import pandas as pd

Blueprint 

df=pd.read_csv('AAPL.csv')

df.head()
Unnamed: 0 symbol date close high low open volume adjClose adjHigh adjLow adjOpen adjVolume divCash splitFactor
0 0 AAPL 2015-05-27 00:00:00+00:00 132.045 132.260 130.05 130.34 45833246 121.682558 121.880685 119.844118 120.111360 45833246 0.0 1.0
1 1 AAPL 2015-05-28 00:00:00+00:00 131.780 131.950 131.10 131.86 30733309 121.438354 121.595013 120.811718 121.512076 30733309 0.0 1.0
2 2 AAPL 2015-05-29 00:00:00+00:00 130.280 131.450 129.90 131.23 50884452 120.056069 121.134251 119.705890 120.931516 50884452 0.0 1.0
3 3 AAPL 2015-06-01 00:00:00+00:00 130.535 131.390 130.05 131.20 32112797 120.291057 121.078960 119.844118 120.903870 32112797 0.0 1.0
4 4 AAPL 2015-06-02 00:00:00+00:00 129.960 130.655 129.32 129.86 33667627 119.761181 120.401640 119.171406 119.669029 33667627 0.0 1.0 
df.tail()
Unnamed: 0 symbol date close high low open volume adjClose adjHigh adjLow adjOpen adjVolume divCash splitFactor
1253 1253 AAPL 2020-05-18 00:00:00+00:00 314.96 316.50 310.3241 313.17 33843125 314.96 316.50 310.3241 313.17 33843125 0.0 1.0
1254 1254 AAPL 2020-05-19 00:00:00+00:00 313.14 318.52 313.0100 315.03 25432385 313.14 318.52 313.0100 315.03 25432385 0.0 1.0
1255 1255 AAPL 2020-05-20 00:00:00+00:00 319.23 319.52 316.2000 316.68 27876215 319.23 319.52 316.2000 316.68 27876215 0.0 1.0
1256 1256 AAPL 2020-05-21 00:00:00+00:00 316.85 320.89 315.8700 318.66 25672211 316.85 320.89 315.8700 318.66 25672211 0.0 1.0
1257 1257 AAPL 2020-05-22 00:00:00+00:00 318.89 319.23 315.3500 315.77 20450754 318.89 319.23 315.3500 315.77 20450754 0.0 1.0

Indexing and Plotting 

df1=df.reset_index()['close']

df1

0       132.045
1       131.780
2       130.280
3       130.535
4       129.960
         ...   
1253    314.960
1254    313.140
1255    319.230
1256    316.850
1257    318.890
Name: close, Length: 1258, dtype: float64
import matplotlib.pyplot as plt
plt.plot(df1)

[<matplotlib.lines.Line2D at 0x2d1a92724e0>]
import numpy as np

df1

0       132.045
1       131.780
2       130.280
3       130.535
4       129.960
         ...   
1253    314.960
1254    313.140
1255    319.230
1256    316.850
1257    318.890
Name: close, Length: 1258, dtype: float64

Scaling 

from sklearn.preprocessing import MinMaxScaler
scaler=MinMaxScaler(feature_range=(0,1))
df1=scaler.fit_transform(np.array(df1).reshape(-1,1))

print(df1)

[[0.17607447]
 [0.17495567]
 [0.16862282]
 ...
 [0.96635143]
 [0.9563033 ]
 [0.96491598]]

Train-Test Split 

training_size=int(len(df1)*0.65)
test_size=len(df1)-training_size
train_data,test_data=df1[0:training_size,:],df1[training_size:len(df1),:1]

training_size,test_size

(817, 441)

Time-Series Window 

import numpy

def create_dataset(dataset, time_step=1):
	dataX, dataY = [], []
	for i in range(len(dataset)-time_step-1):
		a = dataset[i:(i+time_step), 0]   ###i=0, 0,1,2,3-----99   100 
		dataX.append(a)
		dataY.append(dataset[i + time_step, 0])
	return numpy.array(dataX), numpy.array(dataY)

time_step = 100
X_train, y_train = create_dataset(train_data, time_step)
X_test, ytest = create_dataset(test_data, time_step)

print(X_train.shape), print(y_train.shape)

(716, 100)
(716,)

(None, None)
print(X_test.shape), print(ytest.shape)

(340, 100)
(340,)

(None, None)
X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)
X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)

Model Setup and Layers 

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM

model=Sequential()
model.add(LSTM(50,return_sequences=True,input_shape=(100,1)))
model.add(LSTM(50,return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error',optimizer='adam')

model.summary()

Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_7 (LSTM)                (None, 100, 50)           10400     
_________________________________________________________________
lstm_8 (LSTM)                (None, 100, 50)           20200     
_________________________________________________________________
lstm_9 (LSTM)                (None, 50)                20200     
_________________________________________________________________
dense_3 (Dense)              (None, 1)                 51        
=================================================================
Total params: 50,851
Trainable params: 50,851
Non-trainable params: 0
_________________________________________________________________

model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=100,batch_size=64,verbose=1)


Epoch 1/100
12/12 [==============================] - 6s 487ms/step - loss: 0.0206 - val_loss: 0.0505
Epoch 2/100
12/12 [==============================] - 4s 309ms/step - loss: 0.0035 - val_loss: 0.0046
Epoch 3/100
12/12 [==============================] - 4s 300ms/step - loss: 0.0014 - val_loss: 0.0040
Epoch 4/100
12/12 [==============================] - 3s 287ms/step - loss: 8.1361e-04 - val_loss: 0.0073
Epoch 5/100
12/12 [==============================] - 3s 290ms/step - loss: 6.6860e-04 - val_loss: 0.0062
Epoch 6/100
12/12 [==============================] - 3s 255ms/step - loss: 6.4653e-04 - val_loss: 0.0062
Epoch 7/100
12/12 [==============================] - 3s 291ms/step - loss: 6.6186e-04 - val_loss: 0.0062
Epoch 8/100
12/12 [==============================] - 4s 300ms/step - loss: 6.2498e-04 - val_loss: 0.0049
Epoch 9/100
12/12 [==============================] - 4s 297ms/step - loss: 6.2745e-04 - val_loss: 0.0042
Epoch 10/100
12/12 [==============================] - 4s 303ms/step - loss: 6.0206e-04 - val_loss: 0.0050
Epoch 11/100
12/12 [==============================] - 4s 298ms/step - loss: 5.9884e-04 - val_loss: 0.0061
Epoch 12/100
12/12 [==============================] - 4s 304ms/step - loss: 6.1458e-04 - val_loss: 0.0044
Epoch 13/100
12/12 [==============================] - 4s 304ms/step - loss: 5.6830e-04 - val_loss: 0.0041
Epoch 14/100
12/12 [==============================] - 3s 262ms/step - loss: 5.5734e-04 - val_loss: 0.0038
Epoch 15/100
12/12 [==============================] - 3s 244ms/step - loss: 5.5456e-04 - val_loss: 0.0034
Epoch 16/100
12/12 [==============================] - 3s 277ms/step - loss: 5.3865e-04 - val_loss: 0.0034
Epoch 17/100
12/12 [==============================] - 3s 271ms/step - loss: 5.3872e-04 - val_loss: 0.0032
Epoch 18/100
12/12 [==============================] - 3s 260ms/step - loss: 5.2315e-04 - val_loss: 0.0030
Epoch 19/100
12/12 [==============================] - 3s 275ms/step - loss: 5.1791e-04 - val_loss: 0.0029
Epoch 20/100
12/12 [==============================] - 3s 274ms/step - loss: 5.0077e-04 - val_loss: 0.0028
Epoch 21/100
12/12 [==============================] - 3s 273ms/step - loss: 4.8672e-04 - val_loss: 0.0032
Epoch 22/100
12/12 [==============================] - 3s 270ms/step - loss: 4.9148e-04 - val_loss: 0.0026
Epoch 23/100
12/12 [==============================] - 3s 283ms/step - loss: 4.9279e-04 - val_loss: 0.0026
Epoch 24/100
12/12 [==============================] - 4s 308ms/step - loss: 5.2013e-04 - val_loss: 0.0024
Epoch 25/100
12/12 [==============================] - 3s 275ms/step - loss: 5.7301e-04 - val_loss: 0.0024
Epoch 26/100
12/12 [==============================] - 4s 295ms/step - loss: 5.5014e-04 - val_loss: 0.0030
Epoch 27/100
12/12 [==============================] - 4s 301ms/step - loss: 4.8608e-04 - val_loss: 0.0022
Epoch 28/100
12/12 [==============================] - 3s 278ms/step - loss: 4.4525e-04 - val_loss: 0.0022
Epoch 29/100
12/12 [==============================] - 4s 299ms/step - loss: 4.2446e-04 - val_loss: 0.0028
Epoch 30/100
12/12 [==============================] - 4s 302ms/step - loss: 4.9896e-04 - val_loss: 0.0023
Epoch 31/100
12/12 [==============================] - 3s 278ms/step - loss: 4.7568e-04 - val_loss: 0.0022
Epoch 32/100
12/12 [==============================] - 4s 294ms/step - loss: 4.3184e-04 - val_loss: 0.0027
Epoch 33/100
12/12 [==============================] - 4s 292ms/step - loss: 4.1365e-04 - val_loss: 0.0025
Epoch 34/100
12/12 [==============================] - 3s 276ms/step - loss: 4.0967e-04 - val_loss: 0.0022
Epoch 35/100
12/12 [==============================] - 3s 250ms/step - loss: 3.9084e-04 - val_loss: 0.0018
Epoch 36/100
12/12 [==============================] - 3s 291ms/step - loss: 3.8744e-04 - val_loss: 0.0016
Epoch 37/100
12/12 [==============================] - 3s 254ms/step - loss: 3.6441e-04 - val_loss: 0.0024
Epoch 38/100
12/12 [==============================] - 3s 272ms/step - loss: 4.3088e-04 - val_loss: 0.0025
Epoch 39/100
12/12 [==============================] - 3s 259ms/step - loss: 4.1398e-04 - val_loss: 0.0016
Epoch 40/100
12/12 [==============================] - 3s 274ms/step - loss: 3.8981e-04 - val_loss: 0.0016
Epoch 41/100
12/12 [==============================] - 3s 261ms/step - loss: 3.4896e-04 - val_loss: 0.0028
Epoch 42/100
12/12 [==============================] - 3s 282ms/step - loss: 3.7910e-04 - val_loss: 0.0014
Epoch 43/100
12/12 [==============================] - 3s 274ms/step - loss: 3.6404e-04 - val_loss: 0.0022
Epoch 44/100
12/12 [==============================] - 3s 277ms/step - loss: 3.8073e-04 - val_loss: 0.0014
Epoch 45/100
12/12 [==============================] - 3s 276ms/step - loss: 4.0008e-04 - val_loss: 0.0016
Epoch 46/100
12/12 [==============================] - 3s 273ms/step - loss: 4.0253e-04 - val_loss: 0.0015
Epoch 47/100
12/12 [==============================] - 3s 286ms/step - loss: 3.5930e-04 - val_loss: 0.0018
Epoch 48/100
12/12 [==============================] - 3s 264ms/step - loss: 3.0690e-04 - val_loss: 0.0016
Epoch 49/100
12/12 [==============================] - 3s 288ms/step - loss: 3.0504e-04 - val_loss: 0.0022
Epoch 50/100
12/12 [==============================] - 3s 277ms/step - loss: 3.1205e-04 - val_loss: 0.0016
Epoch 51/100
12/12 [==============================] - 3s 291ms/step - loss: 2.8386e-04 - val_loss: 0.0014
Epoch 52/100
12/12 [==============================] - 3s 282ms/step - loss: 2.9832e-04 - val_loss: 0.0016
Epoch 53/100
12/12 [==============================] - 3s 287ms/step - loss: 2.8287e-04 - val_loss: 0.0018
Epoch 54/100
12/12 [==============================] - 3s 286ms/step - loss: 2.8193e-04 - val_loss: 0.0013
Epoch 55/100
12/12 [==============================] - 4s 295ms/step - loss: 2.8989e-04 - val_loss: 0.0026
Epoch 56/100
12/12 [==============================] - 3s 262ms/step - loss: 2.7761e-04 - val_loss: 0.0014
Epoch 57/100
12/12 [==============================] - 3s 270ms/step - loss: 2.6088e-04 - val_loss: 0.0016
Epoch 58/100
12/12 [==============================] - 3s 289ms/step - loss: 2.7300e-04 - val_loss: 0.0013
Epoch 59/100
12/12 [==============================] - 3s 288ms/step - loss: 2.6058e-04 - val_loss: 0.0020
Epoch 60/100
12/12 [==============================] - 3s 285ms/step - loss: 2.5682e-04 - val_loss: 0.0014
Epoch 61/100
12/12 [==============================] - 3s 285ms/step - loss: 2.4091e-04 - val_loss: 0.0013
Epoch 62/100
12/12 [==============================] - 4s 296ms/step - loss: 2.2724e-04 - val_loss: 0.0016
Epoch 63/100
12/12 [==============================] - 3s 258ms/step - loss: 2.3206e-04 - val_loss: 0.0012
Epoch 64/100
12/12 [==============================] - 3s 277ms/step - loss: 2.4468e-04 - val_loss: 0.0014
Epoch 65/100
12/12 [==============================] - 3s 266ms/step - loss: 2.2395e-04 - val_loss: 0.0012
Epoch 66/100
12/12 [==============================] - 3s 263ms/step - loss: 2.1142e-04 - val_loss: 0.0012
Epoch 67/100
12/12 [==============================] - 3s 281ms/step - loss: 2.0540e-04 - val_loss: 0.0016
Epoch 68/100
12/12 [==============================] - 4s 297ms/step - loss: 2.0560e-04 - val_loss: 0.0012
Epoch 69/100
12/12 [==============================] - 3s 218ms/step - loss: 1.9982e-04 - val_loss: 0.0014
Epoch 70/100
12/12 [==============================] - 3s 257ms/step - loss: 2.3622e-04 - val_loss: 0.0015
Epoch 71/100
12/12 [==============================] - 3s 283ms/step - loss: 2.6216e-04 - val_loss: 0.0012
Epoch 72/100
12/12 [==============================] - 3s 282ms/step - loss: 2.4869e-04 - val_loss: 0.0017
Epoch 73/100
12/12 [==============================] - 3s 280ms/step - loss: 2.1853e-04 - val_loss: 0.0013
Epoch 74/100
12/12 [==============================] - 3s 244ms/step - loss: 2.2121e-04 - val_loss: 0.0014
Epoch 75/100
12/12 [==============================] - 3s 283ms/step - loss: 1.9690e-04 - val_loss: 0.0011
Epoch 76/100
12/12 [==============================] - 3s 261ms/step - loss: 2.2144e-04 - val_loss: 0.0011
Epoch 77/100
12/12 [==============================] - 3s 282ms/step - loss: 1.8420e-04 - val_loss: 0.0011
Epoch 78/100
12/12 [==============================] - 3s 282ms/step - loss: 1.7841e-04 - val_loss: 0.0014
Epoch 79/100
12/12 [==============================] - 3s 260ms/step - loss: 1.9611e-04 - val_loss: 0.0013
Epoch 80/100
12/12 [==============================] - 3s 281ms/step - loss: 2.0224e-04 - val_loss: 0.0012
Epoch 81/100
12/12 [==============================] - 3s 290ms/step - loss: 2.1049e-04 - val_loss: 0.0020
Epoch 82/100
12/12 [==============================] - 3s 288ms/step - loss: 1.9466e-04 - val_loss: 0.0010
Epoch 83/100
12/12 [==============================] - 3s 284ms/step - loss: 1.5801e-04 - val_loss: 0.0010
Epoch 84/100
12/12 [==============================] - 3s 272ms/step - loss: 1.6260e-04 - val_loss: 9.4397e-04
Epoch 85/100
12/12 [==============================] - 3s 249ms/step - loss: 1.5695e-04 - val_loss: 0.0013
Epoch 86/100
12/12 [==============================] - 3s 242ms/step - loss: 2.0192e-04 - val_loss: 9.7445e-04
Epoch 87/100
12/12 [==============================] - 3s 271ms/step - loss: 2.2179e-04 - val_loss: 0.0020
Epoch 88/100
12/12 [==============================] - 3s 249ms/step - loss: 2.5509e-04 - val_loss: 0.0015
Epoch 89/100
12/12 [==============================] - 3s 261ms/step - loss: 1.9912e-04 - val_loss: 0.0011
Epoch 90/100
12/12 [==============================] - 3s 265ms/step - loss: 1.6930e-04 - val_loss: 8.9285e-04
Epoch 91/100
12/12 [==============================] - 3s 276ms/step - loss: 1.6435e-04 - val_loss: 9.1264e-04
Epoch 92/100
12/12 [==============================] - 3s 259ms/step - loss: 1.6799e-04 - val_loss: 0.0014
Epoch 93/100
12/12 [==============================] - 3s 282ms/step - loss: 1.9593e-04 - val_loss: 0.0016
Epoch 94/100
12/12 [==============================] - 3s 287ms/step - loss: 1.8104e-04 - val_loss: 0.0010
Epoch 95/100
12/12 [==============================] - 3s 277ms/step - loss: 1.3988e-04 - val_loss: 8.5343e-04
Epoch 96/100
12/12 [==============================] - 3s 280ms/step - loss: 1.4097e-04 - val_loss: 9.3255e-04
Epoch 97/100
12/12 [==============================] - 3s 287ms/step - loss: 1.4070e-04 - val_loss: 8.3848e-04
Epoch 98/100
12/12 [==============================] - 3s 290ms/step - loss: 1.3528e-04 - val_loss: 8.4349e-04
Epoch 99/100
12/12 [==============================] - 3s 288ms/step - loss: 1.4087e-04 - val_loss: 9.8092e-04
Epoch 100/100
12/12 [==============================] - 3s 285ms/step - loss: 1.4775e-04 - val_loss: 9.3230e-04

<tensorflow.python.keras.callbacks.History at 0x2d1aa544a58>

Prediction and Metrics 

train_predict=model.predict(X_train)
test_predict=model.predict(X_test)

train_predict=scaler.inverse_transform(train_predict)
test_predict=scaler.inverse_transform(test_predict)

import math
from sklearn.metrics import mean_squared_error
math.sqrt(mean_squared_error(y_train,train_predict))

140.9909210035748
math.sqrt(mean_squared_error(ytest,test_predict))

235.7193088627771

Present Forecast Plot 

look_back=100
trainPredictPlot = numpy.empty_like(df1)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict

testPredictPlot = numpy.empty_like(df1)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(train_predict)+(look_back*2)+1:len(df1)-1, :] = test_predict

plt.plot(scaler.inverse_transform(df1))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()

len(test_data)

441
x_input=test_data[341:].reshape(1,-1)
x_input.shape

(1, 100)
temp_input=list(x_input)
temp_input=temp_input[0].tolist()

Future Extension 

from numpy import array

lst_output=[]
n_steps=100
i=0
while(i<30):
    
    if(len(temp_input)>100):
        #print(temp_input)
        x_input=np.array(temp_input[1:])
        print("{} day input {}".format(i,x_input))
        x_input=x_input.reshape(1,-1)
        x_input = x_input.reshape((1, n_steps, 1))
        #print(x_input)
        yhat = model.predict(x_input, verbose=0)
        print("{} day output {}".format(i,yhat))
        temp_input.extend(yhat[0].tolist())
        temp_input=temp_input[1:]
        #print(temp_input)
        lst_output.extend(yhat.tolist())
        i=i+1
    else:
        x_input = x_input.reshape((1, n_steps,1))
        yhat = model.predict(x_input, verbose=0)
        print(yhat[0])
        temp_input.extend(yhat[0].tolist())
        print(len(temp_input))
        lst_output.extend(yhat.tolist())
        i=i+1
    

print(lst_output)


[0.94413203]
101
1 day input [0.8866419  0.87431394 0.88431985 0.87836697 0.8986321  0.92582116
 0.92877649 0.95676771 0.93869797 0.93304061 0.94950604 0.96424048
 0.95512117 0.95989192 0.96635143 0.96246728 0.92295027 0.9598497
 0.98792536 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406
 0.99159841 0.96972895 0.97614625 0.96795575 1.         0.99016297
 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037
 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431
 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207
 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013 0.65203074
 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757
 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162
 0.71388162 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292
 0.8289707  0.8125475  0.78776492 0.75162543 0.78426074 0.77974331
 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883
 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211 0.948535
 0.93333615 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004
 0.96635143 0.9563033  0.96491598 0.94413203]
1 day output [[0.9379593]]
2 day input [0.87431394 0.88431985 0.87836697 0.8986321  0.92582116 0.92877649
 0.95676771 0.93869797 0.93304061 0.94950604 0.96424048 0.95512117
 0.95989192 0.96635143 0.96246728 0.92295027 0.9598497  0.98792536
 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841
 0.96972895 0.97614625 0.96795575 1.         0.99016297 0.99050072
 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915
 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225
 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963
 0.7921557  0.64118044 0.68614371 0.66001013 0.65203074 0.58642236
 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642
 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162
 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707
 0.8125475  0.78776492 0.75162543 0.78426074 0.77974331 0.81326522
 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883 0.85628641
 0.87486279 0.88782403 0.90095415 0.92793211 0.948535   0.93333615
 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143
 0.9563033  0.96491598 0.94413203 0.93795931]
2 day output [[0.9286534]]
3 day input [0.88431985 0.87836697 0.8986321  0.92582116 0.92877649 0.95676771
 0.93869797 0.93304061 0.94950604 0.96424048 0.95512117 0.95989192
 0.96635143 0.96246728 0.92295027 0.9598497  0.98792536 0.98594106
 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895
 0.97614625 0.96795575 1.         0.99016297 0.99050072 0.96538039
 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324
 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316
 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557
 0.64118044 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169
 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104
 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506
 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475
 0.78776492 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096
 0.79473106 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279
 0.88782403 0.90095415 0.92793211 0.948535   0.93333615 0.91746179
 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033
 0.96491598 0.94413203 0.93795931 0.92865342]
3 day output [[0.91987926]]
4 day input [0.87836697 0.8986321  0.92582116 0.92877649 0.95676771 0.93869797
 0.93304061 0.94950604 0.96424048 0.95512117 0.95989192 0.96635143
 0.96246728 0.92295027 0.9598497  0.98792536 0.98594106 0.92531453
 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625
 0.96795575 1.         0.99016297 0.99050072 0.96538039 0.98488559
 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823
 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995
 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044
 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673
 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197
 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111
 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492
 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106
 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403
 0.90095415 0.92793211 0.948535   0.93333615 0.91746179 0.92544119
 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598
 0.94413203 0.93795931 0.92865342 0.91987926]
4 day output [[0.9128097]]
5 day input [0.8986321  0.92582116 0.92877649 0.95676771 0.93869797 0.93304061
 0.94950604 0.96424048 0.95512117 0.95989192 0.96635143 0.96246728
 0.92295027 0.9598497  0.98792536 0.98594106 0.92531453 0.92172591
 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575
 1.         0.99016297 0.99050072 0.96538039 0.98488559 0.97086887
 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273
 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725
 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371
 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494
 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402
 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832
 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543
 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148
 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415
 0.92793211 0.948535   0.93333615 0.91746179 0.92544119 0.91771511
 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598 0.94413203
 0.93795931 0.92865342 0.91987926 0.91280973]
5 day output [[0.90777564]]
6 day input [0.92582116 0.92877649 0.95676771 0.93869797 0.93304061 0.94950604
 0.96424048 0.95512117 0.95989192 0.96635143 0.96246728 0.92295027
 0.9598497  0.98792536 0.98594106 0.92531453 0.92172591 0.96474711
 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575 1.
 0.99016297 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007
 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017
 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113
 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013
 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193
 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292
 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832 0.83049059
 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543 0.78426074
 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843
 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211
 0.948535   0.93333615 0.91746179 0.92544119 0.91771511 0.9483239
 0.94064004 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931
 0.92865342 0.91987926 0.91280973 0.90777564]
6 day output [[0.9047326]]
7 day input [0.92877649 0.95676771 0.93869797 0.93304061 0.94950604 0.96424048
 0.95512117 0.95989192 0.96635143 0.96246728 0.92295027 0.9598497
 0.98792536 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406
 0.99159841 0.96972895 0.97614625 0.96795575 1.         0.99016297
 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037
 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431
 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207
 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013 0.65203074
 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757
 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162
 0.71388162 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292
 0.8289707  0.8125475  0.78776492 0.75162543 0.78426074 0.77974331
 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883
 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211 0.948535
 0.93333615 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004
 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342
 0.91987926 0.91280973 0.90777564 0.90473258]
7 day output [[0.9033923]]
8 day input [0.95676771 0.93869797 0.93304061 0.94950604 0.96424048 0.95512117
 0.95989192 0.96635143 0.96246728 0.92295027 0.9598497  0.98792536
 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841
 0.96972895 0.97614625 0.96795575 1.         0.99016297 0.99050072
 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915
 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225
 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963
 0.7921557  0.64118044 0.68614371 0.66001013 0.65203074 0.58642236
 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642
 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162
 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707
 0.8125475  0.78776492 0.75162543 0.78426074 0.77974331 0.81326522
 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883 0.85628641
 0.87486279 0.88782403 0.90095415 0.92793211 0.948535   0.93333615
 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143
 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342 0.91987926
 0.91280973 0.90777564 0.90473258 0.90339231]
8 day output [[0.90332204]]
9 day input [0.93869797 0.93304061 0.94950604 0.96424048 0.95512117 0.95989192
 0.96635143 0.96246728 0.92295027 0.9598497  0.98792536 0.98594106
 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895
 0.97614625 0.96795575 1.         0.99016297 0.99050072 0.96538039
 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324
 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316
 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557
 0.64118044 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169
 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104
 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506
 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475
 0.78776492 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096
 0.79473106 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279
 0.88782403 0.90095415 0.92793211 0.948535   0.93333615 0.91746179
 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033
 0.96491598 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973
 0.90777564 0.90473258 0.90339231 0.90332204]
9 day output [[0.9040391]]
10 day input [0.93304061 0.94950604 0.96424048 0.95512117 0.95989192 0.96635143
 0.96246728 0.92295027 0.9598497  0.98792536 0.98594106 0.92531453
 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625
 0.96795575 1.         0.99016297 0.99050072 0.96538039 0.98488559
 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823
 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995
 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044
 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673
 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197
 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111
 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492
 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106
 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403
 0.90095415 0.92793211 0.948535   0.93333615 0.91746179 0.92544119
 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598
 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564
 0.90473258 0.90339231 0.90332204 0.90403908]
10 day output [[0.9050924]]
11 day input [0.94950604 0.96424048 0.95512117 0.95989192 0.96635143 0.96246728
 0.92295027 0.9598497  0.98792536 0.98594106 0.92531453 0.92172591
 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575
 1.         0.99016297 0.99050072 0.96538039 0.98488559 0.97086887
 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273
 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725
 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371
 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494
 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402
 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832
 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543
 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148
 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415
 0.92793211 0.948535   0.93333615 0.91746179 0.92544119 0.91771511
 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598 0.94413203
 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258
 0.90339231 0.90332204 0.90403908 0.90509242]
11 day output [[0.906118]]
12 day input [0.96424048 0.95512117 0.95989192 0.96635143 0.96246728 0.92295027
 0.9598497  0.98792536 0.98594106 0.92531453 0.92172591 0.96474711
 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575 1.
 0.99016297 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007
 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017
 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113
 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013
 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193
 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292
 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832 0.83049059
 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543 0.78426074
 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843
 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211
 0.948535   0.93333615 0.91746179 0.92544119 0.91771511 0.9483239
 0.94064004 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931
 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258 0.90339231
 0.90332204 0.90403908 0.90509242 0.90611798]
12 day output [[0.90686554]]
13 day input [0.95512117 0.95989192 0.96635143 0.96246728 0.92295027 0.9598497
 0.98792536 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406
 0.99159841 0.96972895 0.97614625 0.96795575 1.         0.99016297
 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037
 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431
 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207
 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013 0.65203074
 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757
 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162
 0.71388162 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292
 0.8289707  0.8125475  0.78776492 0.75162543 0.78426074 0.77974331
 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883
 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211 0.948535
 0.93333615 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004
 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342
 0.91987926 0.91280973 0.90777564 0.90473258 0.90339231 0.90332204
 0.90403908 0.90509242 0.90611798 0.90686554]
13 day output [[0.90720606]]
14 day input [0.95989192 0.96635143 0.96246728 0.92295027 0.9598497  0.98792536
 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841
 0.96972895 0.97614625 0.96795575 1.         0.99016297 0.99050072
 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915
 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225
 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963
 0.7921557  0.64118044 0.68614371 0.66001013 0.65203074 0.58642236
 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642
 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162
 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707
 0.8125475  0.78776492 0.75162543 0.78426074 0.77974331 0.81326522
 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883 0.85628641
 0.87486279 0.88782403 0.90095415 0.92793211 0.948535   0.93333615
 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143
 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342 0.91987926
 0.91280973 0.90777564 0.90473258 0.90339231 0.90332204 0.90403908
 0.90509242 0.90611798 0.90686554 0.90720606]
14 day output [[0.9071163]]
15 day input [0.96635143 0.96246728 0.92295027 0.9598497  0.98792536 0.98594106
 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895
 0.97614625 0.96795575 1.         0.99016297 0.99050072 0.96538039
 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324
 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316
 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557
 0.64118044 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169
 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104
 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506
 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475
 0.78776492 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096
 0.79473106 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279
 0.88782403 0.90095415 0.92793211 0.948535   0.93333615 0.91746179
 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033
 0.96491598 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973
 0.90777564 0.90473258 0.90339231 0.90332204 0.90403908 0.90509242
 0.90611798 0.90686554 0.90720606 0.90711629]
15 day output [[0.9066538]]
16 day input [0.96246728 0.92295027 0.9598497  0.98792536 0.98594106 0.92531453
 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625
 0.96795575 1.         0.99016297 0.99050072 0.96538039 0.98488559
 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823
 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995
 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044
 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673
 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197
 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111
 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492
 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106
 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403
 0.90095415 0.92793211 0.948535   0.93333615 0.91746179 0.92544119
 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598
 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564
 0.90473258 0.90339231 0.90332204 0.90403908 0.90509242 0.90611798
 0.90686554 0.90720606 0.90711629 0.90665382]
16 day output [[0.90592706]]
17 day input [0.92295027 0.9598497  0.98792536 0.98594106 0.92531453 0.92172591
 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575
 1.         0.99016297 0.99050072 0.96538039 0.98488559 0.97086887
 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273
 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725
 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371
 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494
 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402
 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832
 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543
 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148
 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415
 0.92793211 0.948535   0.93333615 0.91746179 0.92544119 0.91771511
 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598 0.94413203
 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258
 0.90339231 0.90332204 0.90403908 0.90509242 0.90611798 0.90686554
 0.90720606 0.90711629 0.90665382 0.90592706]
17 day output [[0.9050646]]
18 day input [0.9598497  0.98792536 0.98594106 0.92531453 0.92172591 0.96474711
 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575 1.
 0.99016297 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007
 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017
 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113
 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013
 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193
 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292
 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832 0.83049059
 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543 0.78426074
 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843
 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211
 0.948535   0.93333615 0.91746179 0.92544119 0.91771511 0.9483239
 0.94064004 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931
 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258 0.90339231
 0.90332204 0.90403908 0.90509242 0.90611798 0.90686554 0.90720606
 0.90711629 0.90665382 0.90592706 0.90506458]
18 day output [[0.90419257]]
19 day input [0.98792536 0.98594106 0.92531453 0.92172591 0.96474711 0.97572406
 0.99159841 0.96972895 0.97614625 0.96795575 1.         0.99016297
 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037
 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431
 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207
 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013 0.65203074
 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757
 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162
 0.71388162 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292
 0.8289707  0.8125475  0.78776492 0.75162543 0.78426074 0.77974331
 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883
 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211 0.948535
 0.93333615 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004
 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342
 0.91987926 0.91280973 0.90777564 0.90473258 0.90339231 0.90332204
 0.90403908 0.90509242 0.90611798 0.90686554 0.90720606 0.90711629
 0.90665382 0.90592706 0.90506458 0.90419257]
19 day output [[0.9034131]]
20 day input [0.98594106 0.92531453 0.92172591 0.96474711 0.97572406 0.99159841
 0.96972895 0.97614625 0.96795575 1.         0.99016297 0.99050072
 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915
 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225
 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963
 0.7921557  0.64118044 0.68614371 0.66001013 0.65203074 0.58642236
 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642
 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162
 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707
 0.8125475  0.78776492 0.75162543 0.78426074 0.77974331 0.81326522
 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883 0.85628641
 0.87486279 0.88782403 0.90095415 0.92793211 0.948535   0.93333615
 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143
 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342 0.91987926
 0.91280973 0.90777564 0.90473258 0.90339231 0.90332204 0.90403908
 0.90509242 0.90611798 0.90686554 0.90720606 0.90711629 0.90665382
 0.90592706 0.90506458 0.90419257 0.90341312]
20 day output [[0.90279734]]
21 day input [0.92531453 0.92172591 0.96474711 0.97572406 0.99159841 0.96972895
 0.97614625 0.96795575 1.         0.99016297 0.99050072 0.96538039
 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324
 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316
 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557
 0.64118044 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169
 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104
 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506
 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475
 0.78776492 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096
 0.79473106 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279
 0.88782403 0.90095415 0.92793211 0.948535   0.93333615 0.91746179
 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033
 0.96491598 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973
 0.90777564 0.90473258 0.90339231 0.90332204 0.90403908 0.90509242
 0.90611798 0.90686554 0.90720606 0.90711629 0.90665382 0.90592706
 0.90506458 0.90419257 0.90341312 0.90279734]
21 day output [[0.9023812]]
22 day input [0.92172591 0.96474711 0.97572406 0.99159841 0.96972895 0.97614625
 0.96795575 1.         0.99016297 0.99050072 0.96538039 0.98488559
 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823
 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995
 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044
 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673
 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197
 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111
 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492
 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106
 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403
 0.90095415 0.92793211 0.948535   0.93333615 0.91746179 0.92544119
 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598
 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564
 0.90473258 0.90339231 0.90332204 0.90403908 0.90509242 0.90611798
 0.90686554 0.90720606 0.90711629 0.90665382 0.90592706 0.90506458
 0.90419257 0.90341312 0.90279734 0.90238118]
22 day output [[0.9021694]]
23 day input [0.96474711 0.97572406 0.99159841 0.96972895 0.97614625 0.96795575
 1.         0.99016297 0.99050072 0.96538039 0.98488559 0.97086887
 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273
 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725
 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371
 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494
 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402
 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832
 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543
 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148
 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415
 0.92793211 0.948535   0.93333615 0.91746179 0.92544119 0.91771511
 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598 0.94413203
 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258
 0.90339231 0.90332204 0.90403908 0.90509242 0.90611798 0.90686554
 0.90720606 0.90711629 0.90665382 0.90592706 0.90506458 0.90419257
 0.90341312 0.90279734 0.90238118 0.90216941]
23 day output [[0.90213937]]
24 day input [0.97572406 0.99159841 0.96972895 0.97614625 0.96795575 1.
 0.99016297 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007
 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017
 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113
 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013
 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193
 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292
 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832 0.83049059
 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543 0.78426074
 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843
 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211
 0.948535   0.93333615 0.91746179 0.92544119 0.91771511 0.9483239
 0.94064004 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931
 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258 0.90339231
 0.90332204 0.90403908 0.90509242 0.90611798 0.90686554 0.90720606
 0.90711629 0.90665382 0.90592706 0.90506458 0.90419257 0.90341312
 0.90279734 0.90238118 0.90216941 0.90213937]
24 day output [[0.9022528]]
25 day input [0.99159841 0.96972895 0.97614625 0.96795575 1.         0.99016297
 0.99050072 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037
 0.83483915 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431
 0.89673225 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207
 0.6665963  0.7921557  0.64118044 0.68614371 0.66001013 0.65203074
 0.58642236 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757
 0.69437642 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162
 0.71388162 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292
 0.8289707  0.8125475  0.78776492 0.75162543 0.78426074 0.77974331
 0.81326522 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883
 0.85628641 0.87486279 0.88782403 0.90095415 0.92793211 0.948535
 0.93333615 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004
 0.96635143 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342
 0.91987926 0.91280973 0.90777564 0.90473258 0.90339231 0.90332204
 0.90403908 0.90509242 0.90611798 0.90686554 0.90720606 0.90711629
 0.90665382 0.90592706 0.90506458 0.90419257 0.90341312 0.90279734
 0.90238118 0.90216941 0.90213937 0.90225279]
25 day output [[0.90246403]]
26 day input [0.96972895 0.97614625 0.96795575 1.         0.99016297 0.99050072
 0.96538039 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915
 0.85413324 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225
 0.85527316 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963
 0.7921557  0.64118044 0.68614371 0.66001013 0.65203074 0.58642236
 0.56586169 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642
 0.69218104 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162
 0.74191506 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707
 0.8125475  0.78776492 0.75162543 0.78426074 0.77974331 0.81326522
 0.8141096  0.79473106 0.83336148 0.85898843 0.83901883 0.85628641
 0.87486279 0.88782403 0.90095415 0.92793211 0.948535   0.93333615
 0.91746179 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143
 0.9563033  0.96491598 0.94413203 0.93795931 0.92865342 0.91987926
 0.91280973 0.90777564 0.90473258 0.90339231 0.90332204 0.90403908
 0.90509242 0.90611798 0.90686554 0.90720606 0.90711629 0.90665382
 0.90592706 0.90506458 0.90419257 0.90341312 0.90279734 0.90238118
 0.90216941 0.90213937 0.90225279 0.90246403]
26 day output [[0.90272856]]
27 day input [0.97614625 0.96795575 1.         0.99016297 0.99050072 0.96538039
 0.98488559 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324
 0.77336823 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316
 0.83884995 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557
 0.64118044 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169
 0.66089673 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104
 0.63569197 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506
 0.75002111 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475
 0.78776492 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096
 0.79473106 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279
 0.88782403 0.90095415 0.92793211 0.948535   0.93333615 0.91746179
 0.92544119 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033
 0.96491598 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973
 0.90777564 0.90473258 0.90339231 0.90332204 0.90403908 0.90509242
 0.90611798 0.90686554 0.90720606 0.90711629 0.90665382 0.90592706
 0.90506458 0.90419257 0.90341312 0.90279734 0.90238118 0.90216941
 0.90213937 0.90225279 0.90246403 0.90272856]
27 day output [[0.90300757]]
28 day input [0.96795575 1.         0.99016297 0.99050072 0.96538039 0.98488559
 0.97086887 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823
 0.77269273 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995
 0.74233725 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044
 0.68614371 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673
 0.65515494 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197
 0.65266402 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111
 0.77222832 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492
 0.75162543 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106
 0.83336148 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403
 0.90095415 0.92793211 0.948535   0.93333615 0.91746179 0.92544119
 0.91771511 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598
 0.94413203 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564
 0.90473258 0.90339231 0.90332204 0.90403908 0.90509242 0.90611798
 0.90686554 0.90720606 0.90711629 0.90665382 0.90592706 0.90506458
 0.90419257 0.90341312 0.90279734 0.90238118 0.90216941 0.90213937
 0.90225279 0.90246403 0.90272856 0.90300757]
28 day output [[0.903272]]
29 day input [1.         0.99016297 0.99050072 0.96538039 0.98488559 0.97086887
 0.94026007 0.87748037 0.83483915 0.85413324 0.77336823 0.77269273
 0.88014017 0.84007431 0.89673225 0.85527316 0.83884995 0.74233725
 0.82327113 0.78143207 0.6665963  0.7921557  0.64118044 0.68614371
 0.66001013 0.65203074 0.58642236 0.56586169 0.66089673 0.65515494
 0.70970193 0.66452757 0.69437642 0.69218104 0.63569197 0.65266402
 0.63780292 0.7267162  0.71388162 0.74191506 0.75002111 0.77222832
 0.83049059 0.8194292  0.8289707  0.8125475  0.78776492 0.75162543
 0.78426074 0.77974331 0.81326522 0.8141096  0.79473106 0.83336148
 0.85898843 0.83901883 0.85628641 0.87486279 0.88782403 0.90095415
 0.92793211 0.948535   0.93333615 0.91746179 0.92544119 0.91771511
 0.9483239  0.94064004 0.96635143 0.9563033  0.96491598 0.94413203
 0.93795931 0.92865342 0.91987926 0.91280973 0.90777564 0.90473258
 0.90339231 0.90332204 0.90403908 0.90509242 0.90611798 0.90686554
 0.90720606 0.90711629 0.90665382 0.90592706 0.90506458 0.90419257
 0.90341312 0.90279734 0.90238118 0.90216941 0.90213937 0.90225279
 0.90246403 0.90272856 0.90300757 0.90327197]
29 day output [[0.90350425]]
[[0.9441320300102234], [0.9379593133926392], [0.9286534190177917], [0.9198792576789856], [0.9128097295761108], [0.9077756404876709], [0.9047325849533081], [0.9033923149108887], [0.9033220410346985], [0.9040390849113464], [0.9050924181938171], [0.9061179757118225], [0.9068655371665955], [0.9072060585021973], [0.9071162939071655], [0.9066538214683533], [0.9059270620346069], [0.905064582824707], [0.9041925668716431], [0.9034131169319153], [0.9027973413467407], [0.902381181716919], [0.902169406414032], [0.9021393656730652], [0.9022527933120728], [0.9024640321731567], [0.9027285575866699], [0.9030075669288635], [0.9032719731330872], [0.9035042524337769]]

Predicting the Next 30 Days

DataFrame Split 

day_new=np.arange(1,101)
day_pred=np.arange(101,131)

import matplotlib.pyplot as plt

len(df1)

1258

Extended Plots 

plt.plot(day_new,scaler.inverse_transform(df1[1158:]))
plt.plot(day_pred,scaler.inverse_transform(lst_output))

[<matplotlib.lines.Line2D at 0x2d1b0f352b0>]
df3=df1.tolist()
df3.extend(lst_output)
plt.plot(df3[1200:])

[<matplotlib.lines.Line2D at 0x2d1b0f55ac8>]
df3=scaler.inverse_transform(df3).tolist()

plt.plot(df3)

[<matplotlib.lines.Line2D at 0x2d1a904c470>]