Este Script en Python, utiliza el analisis de datos para visualizar las ventas de acciones en la bolsa de Nueva York, ademas realiza predicciones basado en estos datos para el futuro. 🤚 Utiliza los paquetes : yfinance -> para obtener el precio de las acciones, pandas -> para poder crear el data set con el precio de las acciones y poder modelar los datos a partir de estas, numpy -> para poder analizar los datos de las acciones , matplotlib.pyplot -> para poder modelar los datos de las acciones, SKlearn.preprocessing -> para elegir el modelo(pre-procesado) de aprendizaje automatico para que realice las predicciones de los precios -> el modulo de este paquete que se utilizo fue MinMaxScaler, Keras -> utiliza el modelo a entrenar con los datos del set de datos y los datos pre-procesados
In [ ]:
# Importar las bibliotecas necesarias
import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import LSTM, Dense
In [ ]:
# se toman los valores comezando el 2023-01-13 hasta el 2023-12-13
# Descargar los datos de precios de las acciones de Amazon
data = yf.download('AMZN', start='2023-01-13', interval='1h', end='2023-12-13', progress=False)[['Close']]
data.head()
Out[ ]:
| Close | |
|---|---|
| Datetime | |
| 2023-01-13 09:30:00-05:00 | 96.614998 |
| 2023-01-13 10:30:00-05:00 | 95.980003 |
| 2023-01-13 11:30:00-05:00 | 96.940102 |
| 2023-01-13 12:30:00-05:00 | 97.769997 |
| 2023-01-13 13:30:00-05:00 | 98.224998 |
In [ ]:
# Visualizar los datos
data.plot(figsize=(10,10))
# Escalar los datos entre 0 y 1
cl = data.Close.astype('float32')
train = cl[0:int(len(cl)*0.90)]
scl = MinMaxScaler()
scl.fit(train.values.reshape(-1,1))
cl = scl.transform(cl.values.reshape(-1,1))
In [ ]:
# Crear una función para procesar los datos en segmentos de 10 observaciones
def processData(data, lb):
X, Y = [], []
for i in range(len(data)-lb-1):
X.append(data[i:(i+lb), 0])
Y.append(data[(i+lb), 0])
return np.array(X), np.array(Y)
lb = 10
X, y = processData(cl, lb)
In [ ]:
# Dividir los datos en conjuntos de entrenamiento y prueba (90-10)
X_train, X_test = X[:int(X.shape[0]*0.90)], X[int(X.shape[0]*0.90):]
y_train, y_test = y[:int(y.shape[0]*0.90)], y[int(y.shape[0]*0.90):]
print(X_train.shape, X_train.shape)
print(X_test.shape, X_test.shape)
print(y_train.shape)
print(y_test.shape)
(1431, 10) (1431, 10) (160, 10) (160, 10) (1431,) (160,)
In [ ]:
# Construir el modelo LSTM con una capa oculta de 256 neuronas y una capa de salida de una neurona
model = Sequential()
model.add(LSTM(256, input_shape=(lb, 1)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
In [ ]:
# Reajustar los datos para que tengan la forma (Muestra, Paso, Característica)
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))
In [ ]:
# Entrenar el modelo con los datos de entrenamiento y validar con los datos de prueba
history = model.fit(X_train, y_train, epochs=300, validation_data=(X_test, y_test), shuffle=False)
model.summary()
Epoch 1/300
45/45 [==============================] - 9s 23ms/step - loss: 0.0030 - val_loss: 0.0011
Epoch 2/300
45/45 [==============================] - 0s 8ms/step - loss: 0.0022 - val_loss: 0.0033
Epoch 3/300
45/45 [==============================] - 0s 8ms/step - loss: 0.0023 - val_loss: 7.9756e-04
Epoch 4/300
45/45 [==============================] - 0s 7ms/step - loss: 0.0027 - val_loss: 9.9635e-04
Epoch 5/300
45/45 [==============================] - 0s 8ms/step - loss: 0.0014 - val_loss: 0.0022
Epoch 6/300
45/45 [==============================] - 0s 8ms/step - loss: 8.9955e-04 - val_loss: 0.0018
Epoch 7/300
45/45 [==============================] - 0s 10ms/step - loss: 8.4251e-04 - val_loss: 0.0016
Epoch 8/300
45/45 [==============================] - 0s 5ms/step - loss: 9.7083e-04 - val_loss: 0.0018
Epoch 9/300
45/45 [==============================] - 0s 5ms/step - loss: 0.0010 - val_loss: 0.0017
Epoch 10/300
45/45 [==============================] - 0s 9ms/step - loss: 0.0011 - val_loss: 0.0017
Epoch 11/300
45/45 [==============================] - 0s 6ms/step - loss: 9.8881e-04 - val_loss: 0.0016
Epoch 12/300
45/45 [==============================] - 0s 5ms/step - loss: 8.6830e-04 - val_loss: 0.0014
Epoch 13/300
45/45 [==============================] - 0s 5ms/step - loss: 7.9933e-04 - val_loss: 0.0013
Epoch 14/300
45/45 [==============================] - 1s 25ms/step - loss: 7.8437e-04 - val_loss: 0.0012
Epoch 15/300
45/45 [==============================] - 0s 10ms/step - loss: 7.8388e-04 - val_loss: 0.0012
Epoch 16/300
45/45 [==============================] - 0s 6ms/step - loss: 7.8056e-04 - val_loss: 0.0011
Epoch 17/300
45/45 [==============================] - 0s 6ms/step - loss: 7.5276e-04 - val_loss: 0.0010
Epoch 18/300
45/45 [==============================] - 0s 6ms/step - loss: 7.0621e-04 - val_loss: 9.7022e-04
Epoch 19/300
45/45 [==============================] - 0s 6ms/step - loss: 6.5878e-04 - val_loss: 8.9600e-04
Epoch 20/300
45/45 [==============================] - 0s 6ms/step - loss: 6.2457e-04 - val_loss: 8.3813e-04
Epoch 21/300
45/45 [==============================] - 0s 9ms/step - loss: 6.0222e-04 - val_loss: 7.9679e-04
Epoch 22/300
45/45 [==============================] - 0s 6ms/step - loss: 5.8620e-04 - val_loss: 7.6591e-04
Epoch 23/300
45/45 [==============================] - 0s 6ms/step - loss: 5.7165e-04 - val_loss: 7.3956e-04
Epoch 24/300
45/45 [==============================] - 0s 9ms/step - loss: 5.5687e-04 - val_loss: 7.1472e-04
Epoch 25/300
45/45 [==============================] - 0s 6ms/step - loss: 5.4239e-04 - val_loss: 6.9079e-04
Epoch 26/300
45/45 [==============================] - 0s 6ms/step - loss: 5.2945e-04 - val_loss: 6.6813e-04
Epoch 27/300
45/45 [==============================] - 0s 10ms/step - loss: 5.1849e-04 - val_loss: 6.4693e-04
Epoch 28/300
45/45 [==============================] - 0s 6ms/step - loss: 5.0935e-04 - val_loss: 6.2737e-04
Epoch 29/300
45/45 [==============================] - 0s 5ms/step - loss: 5.0164e-04 - val_loss: 6.0963e-04
Epoch 30/300
45/45 [==============================] - 0s 6ms/step - loss: 4.9496e-04 - val_loss: 5.9390e-04
Epoch 31/300
45/45 [==============================] - 0s 8ms/step - loss: 4.8905e-04 - val_loss: 5.8040e-04
Epoch 32/300
45/45 [==============================] - 0s 8ms/step - loss: 4.8369e-04 - val_loss: 5.6917e-04
Epoch 33/300
45/45 [==============================] - 1s 12ms/step - loss: 4.7880e-04 - val_loss: 5.6008e-04
Epoch 34/300
45/45 [==============================] - 0s 8ms/step - loss: 4.7424e-04 - val_loss: 5.5264e-04
Epoch 35/300
45/45 [==============================] - 1s 13ms/step - loss: 4.6993e-04 - val_loss: 5.4681e-04
Epoch 36/300
45/45 [==============================] - 0s 8ms/step - loss: 4.6582e-04 - val_loss: 5.4188e-04
Epoch 37/300
45/45 [==============================] - 0s 8ms/step - loss: 4.6153e-04 - val_loss: 5.3713e-04
Epoch 38/300
45/45 [==============================] - 0s 8ms/step - loss: 4.5694e-04 - val_loss: 5.3204e-04
Epoch 39/300
45/45 [==============================] - 0s 7ms/step - loss: 4.5184e-04 - val_loss: 5.2688e-04
Epoch 40/300
45/45 [==============================] - 0s 9ms/step - loss: 4.4614e-04 - val_loss: 5.1936e-04
Epoch 41/300
45/45 [==============================] - 0s 5ms/step - loss: 4.3807e-04 - val_loss: 5.0343e-04
Epoch 42/300
45/45 [==============================] - 0s 6ms/step - loss: 4.2919e-04 - val_loss: 4.8649e-04
Epoch 43/300
45/45 [==============================] - 0s 6ms/step - loss: 4.2300e-04 - val_loss: 4.7477e-04
Epoch 44/300
45/45 [==============================] - 0s 5ms/step - loss: 4.1978e-04 - val_loss: 4.5573e-04
Epoch 45/300
45/45 [==============================] - 0s 6ms/step - loss: 4.1823e-04 - val_loss: 4.6017e-04
Epoch 46/300
45/45 [==============================] - 0s 6ms/step - loss: 4.2304e-04 - val_loss: 4.3177e-04
Epoch 47/300
45/45 [==============================] - 0s 6ms/step - loss: 4.1826e-04 - val_loss: 4.1488e-04
Epoch 48/300
45/45 [==============================] - 0s 6ms/step - loss: 4.2830e-04 - val_loss: 4.2251e-04
Epoch 49/300
45/45 [==============================] - 0s 9ms/step - loss: 4.3618e-04 - val_loss: 3.8520e-04
Epoch 50/300
45/45 [==============================] - 0s 6ms/step - loss: 4.2401e-04 - val_loss: 3.5267e-04
Epoch 51/300
45/45 [==============================] - 0s 6ms/step - loss: 4.3648e-04 - val_loss: 3.7387e-04
Epoch 52/300
45/45 [==============================] - 0s 10ms/step - loss: 4.7889e-04 - val_loss: 3.8456e-04
Epoch 53/300
45/45 [==============================] - 0s 5ms/step - loss: 4.9331e-04 - val_loss: 3.8027e-04
Epoch 54/300
45/45 [==============================] - 0s 6ms/step - loss: 5.4718e-04 - val_loss: 5.2040e-04
Epoch 55/300
45/45 [==============================] - 0s 6ms/step - loss: 6.6606e-04 - val_loss: 2.8358e-04
Epoch 56/300
45/45 [==============================] - 0s 10ms/step - loss: 7.3407e-04 - val_loss: 3.6308e-04
Epoch 57/300
45/45 [==============================] - 0s 5ms/step - loss: 0.0011 - val_loss: 0.0014
Epoch 58/300
45/45 [==============================] - 0s 6ms/step - loss: 0.0012 - val_loss: 2.8392e-04
Epoch 59/300
45/45 [==============================] - 0s 9ms/step - loss: 8.0486e-04 - val_loss: 2.7804e-04
Epoch 60/300
45/45 [==============================] - 0s 5ms/step - loss: 0.0011 - val_loss: 4.2736e-04
Epoch 61/300
45/45 [==============================] - 0s 6ms/step - loss: 6.2067e-04 - val_loss: 3.3759e-04
Epoch 62/300
45/45 [==============================] - 0s 6ms/step - loss: 4.0672e-04 - val_loss: 3.0050e-04
Epoch 63/300
45/45 [==============================] - 0s 6ms/step - loss: 3.2599e-04 - val_loss: 2.9895e-04
Epoch 64/300
45/45 [==============================] - 0s 6ms/step - loss: 3.3227e-04 - val_loss: 2.9880e-04
Epoch 65/300
45/45 [==============================] - 0s 5ms/step - loss: 3.2354e-04 - val_loss: 3.0037e-04
Epoch 66/300
45/45 [==============================] - 0s 6ms/step - loss: 3.2324e-04 - val_loss: 3.0459e-04
Epoch 67/300
45/45 [==============================] - 0s 6ms/step - loss: 3.2293e-04 - val_loss: 3.1170e-04
Epoch 68/300
45/45 [==============================] - 0s 6ms/step - loss: 3.2353e-04 - val_loss: 3.1932e-04
Epoch 69/300
45/45 [==============================] - 0s 5ms/step - loss: 3.2410e-04 - val_loss: 3.2612e-04
Epoch 70/300
45/45 [==============================] - 0s 5ms/step - loss: 3.2445e-04 - val_loss: 3.3132e-04
Epoch 71/300
45/45 [==============================] - 0s 11ms/step - loss: 3.2467e-04 - val_loss: 3.3538e-04
Epoch 72/300
45/45 [==============================] - 0s 8ms/step - loss: 3.2483e-04 - val_loss: 3.3890e-04
Epoch 73/300
45/45 [==============================] - 0s 8ms/step - loss: 3.2498e-04 - val_loss: 3.4211e-04
Epoch 74/300
45/45 [==============================] - 1s 26ms/step - loss: 3.2510e-04 - val_loss: 3.4498e-04
Epoch 75/300
45/45 [==============================] - 0s 8ms/step - loss: 3.2520e-04 - val_loss: 3.4757e-04
Epoch 76/300
45/45 [==============================] - 0s 8ms/step - loss: 3.2531e-04 - val_loss: 3.4993e-04
Epoch 77/300
45/45 [==============================] - 0s 5ms/step - loss: 3.2544e-04 - val_loss: 3.5214e-04
Epoch 78/300
45/45 [==============================] - 0s 5ms/step - loss: 3.2557e-04 - val_loss: 3.5418e-04
Epoch 79/300
45/45 [==============================] - 0s 5ms/step - loss: 3.2573e-04 - val_loss: 3.5611e-04
Epoch 80/300
45/45 [==============================] - 0s 6ms/step - loss: 3.2592e-04 - val_loss: 3.5794e-04
Epoch 81/300
45/45 [==============================] - 0s 10ms/step - loss: 3.2614e-04 - val_loss: 3.5968e-04
Epoch 82/300
45/45 [==============================] - 0s 5ms/step - loss: 3.2641e-04 - val_loss: 3.6133e-04
Epoch 83/300
45/45 [==============================] - 0s 5ms/step - loss: 3.2674e-04 - val_loss: 3.6289e-04
Epoch 84/300
45/45 [==============================] - 0s 9ms/step - loss: 3.2716e-04 - val_loss: 3.6436e-04
Epoch 85/300
45/45 [==============================] - 0s 6ms/step - loss: 3.2768e-04 - val_loss: 3.6571e-04
Epoch 86/300
45/45 [==============================] - 0s 5ms/step - loss: 3.2834e-04 - val_loss: 3.6695e-04
Epoch 87/300
45/45 [==============================] - 0s 10ms/step - loss: 3.2919e-04 - val_loss: 3.6807e-04
Epoch 88/300
45/45 [==============================] - 0s 5ms/step - loss: 3.3028e-04 - val_loss: 3.6909e-04
Epoch 89/300
45/45 [==============================] - 0s 5ms/step - loss: 3.3168e-04 - val_loss: 3.7002e-04
Epoch 90/300
45/45 [==============================] - 0s 6ms/step - loss: 3.3346e-04 - val_loss: 3.7091e-04
Epoch 91/300
45/45 [==============================] - 2s 36ms/step - loss: 3.3570e-04 - val_loss: 3.7182e-04
Epoch 92/300
45/45 [==============================] - 0s 6ms/step - loss: 3.3849e-04 - val_loss: 3.7282e-04
Epoch 93/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4190e-04 - val_loss: 3.7400e-04
Epoch 94/300
45/45 [==============================] - 2s 42ms/step - loss: 3.4598e-04 - val_loss: 3.7544e-04
Epoch 95/300
45/45 [==============================] - 0s 10ms/step - loss: 3.5076e-04 - val_loss: 3.7721e-04
Epoch 96/300
45/45 [==============================] - 0s 5ms/step - loss: 3.5619e-04 - val_loss: 3.7937e-04
Epoch 97/300
45/45 [==============================] - 0s 6ms/step - loss: 3.6213e-04 - val_loss: 3.8192e-04
Epoch 98/300
45/45 [==============================] - 0s 8ms/step - loss: 3.6832e-04 - val_loss: 3.8480e-04
Epoch 99/300
45/45 [==============================] - 1s 12ms/step - loss: 3.7433e-04 - val_loss: 3.8784e-04
Epoch 100/300
45/45 [==============================] - 0s 7ms/step - loss: 3.7961e-04 - val_loss: 3.9074e-04
Epoch 101/300
45/45 [==============================] - 1s 13ms/step - loss: 3.8350e-04 - val_loss: 3.9310e-04
Epoch 102/300
45/45 [==============================] - 0s 7ms/step - loss: 3.8544e-04 - val_loss: 3.9453e-04
Epoch 103/300
45/45 [==============================] - 0s 7ms/step - loss: 3.8511e-04 - val_loss: 3.9473e-04
Epoch 104/300
45/45 [==============================] - 2s 44ms/step - loss: 3.8262e-04 - val_loss: 3.9365e-04
Epoch 105/300
45/45 [==============================] - 0s 10ms/step - loss: 3.7858e-04 - val_loss: 3.9150e-04
Epoch 106/300
45/45 [==============================] - 0s 6ms/step - loss: 3.7382e-04 - val_loss: 3.8865e-04
Epoch 107/300
45/45 [==============================] - 0s 5ms/step - loss: 3.6914e-04 - val_loss: 3.8553e-04
Epoch 108/300
45/45 [==============================] - 1s 23ms/step - loss: 3.6509e-04 - val_loss: 3.8249e-04
Epoch 109/300
45/45 [==============================] - 1s 28ms/step - loss: 3.6191e-04 - val_loss: 3.7973e-04
Epoch 110/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5962e-04 - val_loss: 3.7733e-04
Epoch 111/300
45/45 [==============================] - 0s 5ms/step - loss: 3.5814e-04 - val_loss: 3.7532e-04
Epoch 112/300
45/45 [==============================] - 0s 10ms/step - loss: 3.5732e-04 - val_loss: 3.7365e-04
Epoch 113/300
45/45 [==============================] - 1s 22ms/step - loss: 3.5702e-04 - val_loss: 3.7229e-04
Epoch 114/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5711e-04 - val_loss: 3.7119e-04
Epoch 115/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5749e-04 - val_loss: 3.7030e-04
Epoch 116/300
45/45 [==============================] - 0s 5ms/step - loss: 3.5802e-04 - val_loss: 3.6959e-04
Epoch 117/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5862e-04 - val_loss: 3.6900e-04
Epoch 118/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5920e-04 - val_loss: 3.6849e-04
Epoch 119/300
45/45 [==============================] - 0s 10ms/step - loss: 3.5965e-04 - val_loss: 3.6800e-04
Epoch 120/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5992e-04 - val_loss: 3.6749e-04
Epoch 121/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5994e-04 - val_loss: 3.6691e-04
Epoch 122/300
45/45 [==============================] - 0s 5ms/step - loss: 3.5968e-04 - val_loss: 3.6622e-04
Epoch 123/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5914e-04 - val_loss: 3.6539e-04
Epoch 124/300
45/45 [==============================] - 1s 29ms/step - loss: 3.5833e-04 - val_loss: 3.6442e-04
Epoch 125/300
45/45 [==============================] - 0s 8ms/step - loss: 3.5731e-04 - val_loss: 3.6329e-04
Epoch 126/300
45/45 [==============================] - 0s 7ms/step - loss: 3.5612e-04 - val_loss: 3.6204e-04
Epoch 127/300
45/45 [==============================] - 1s 28ms/step - loss: 3.5484e-04 - val_loss: 3.6067e-04
Epoch 128/300
45/45 [==============================] - 1s 22ms/step - loss: 3.5353e-04 - val_loss: 3.5922e-04
Epoch 129/300
45/45 [==============================] - 0s 5ms/step - loss: 3.5224e-04 - val_loss: 3.5773e-04
Epoch 130/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5101e-04 - val_loss: 3.5621e-04
Epoch 131/300
45/45 [==============================] - 0s 5ms/step - loss: 3.4987e-04 - val_loss: 3.5468e-04
Epoch 132/300
45/45 [==============================] - 1s 29ms/step - loss: 3.4883e-04 - val_loss: 3.5318e-04
Epoch 133/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4790e-04 - val_loss: 3.5171e-04
Epoch 134/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4708e-04 - val_loss: 3.5028e-04
Epoch 135/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4635e-04 - val_loss: 3.4889e-04
Epoch 136/300
45/45 [==============================] - 1s 22ms/step - loss: 3.4570e-04 - val_loss: 3.4754e-04
Epoch 137/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4513e-04 - val_loss: 3.4624e-04
Epoch 138/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4461e-04 - val_loss: 3.4497e-04
Epoch 139/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4414e-04 - val_loss: 3.4374e-04
Epoch 140/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4370e-04 - val_loss: 3.4255e-04
Epoch 141/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4327e-04 - val_loss: 3.4138e-04
Epoch 142/300
45/45 [==============================] - 1s 11ms/step - loss: 3.4286e-04 - val_loss: 3.4023e-04
Epoch 143/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4244e-04 - val_loss: 3.3909e-04
Epoch 144/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4202e-04 - val_loss: 3.3796e-04
Epoch 145/300
45/45 [==============================] - 0s 5ms/step - loss: 3.4159e-04 - val_loss: 3.3684e-04
Epoch 146/300
45/45 [==============================] - 1s 27ms/step - loss: 3.4114e-04 - val_loss: 3.3572e-04
Epoch 147/300
45/45 [==============================] - 0s 8ms/step - loss: 3.4066e-04 - val_loss: 3.3459e-04
Epoch 148/300
45/45 [==============================] - 0s 8ms/step - loss: 3.4017e-04 - val_loss: 3.3345e-04
Epoch 149/300
45/45 [==============================] - 0s 8ms/step - loss: 3.3966e-04 - val_loss: 3.3230e-04
Epoch 150/300
45/45 [==============================] - 0s 9ms/step - loss: 3.3912e-04 - val_loss: 3.3114e-04
Epoch 151/300
45/45 [==============================] - 1s 28ms/step - loss: 3.3857e-04 - val_loss: 3.2995e-04
Epoch 152/300
45/45 [==============================] - 0s 9ms/step - loss: 3.3800e-04 - val_loss: 3.2875e-04
Epoch 153/300
45/45 [==============================] - 1s 6ms/step - loss: 3.3742e-04 - val_loss: 3.2752e-04
Epoch 154/300
45/45 [==============================] - 0s 6ms/step - loss: 3.3684e-04 - val_loss: 3.2627e-04
Epoch 155/300
45/45 [==============================] - 0s 6ms/step - loss: 3.3626e-04 - val_loss: 3.2501e-04
Epoch 156/300
45/45 [==============================] - 0s 6ms/step - loss: 3.3568e-04 - val_loss: 3.2372e-04
Epoch 157/300
45/45 [==============================] - 0s 9ms/step - loss: 3.3510e-04 - val_loss: 3.2241e-04
Epoch 158/300
45/45 [==============================] - 0s 6ms/step - loss: 3.3454e-04 - val_loss: 3.2107e-04
Epoch 159/300
45/45 [==============================] - 0s 6ms/step - loss: 3.3399e-04 - val_loss: 3.1964e-04
Epoch 160/300
45/45 [==============================] - 0s 9ms/step - loss: 3.3343e-04 - val_loss: 3.1800e-04
Epoch 161/300
45/45 [==============================] - 0s 5ms/step - loss: 3.3283e-04 - val_loss: 3.1609e-04
Epoch 162/300
45/45 [==============================] - 0s 6ms/step - loss: 3.3231e-04 - val_loss: 3.1441e-04
Epoch 163/300
45/45 [==============================] - 0s 5ms/step - loss: 3.3194e-04 - val_loss: 3.1294e-04
Epoch 164/300
45/45 [==============================] - 1s 31ms/step - loss: 3.3151e-04 - val_loss: 3.1145e-04
Epoch 165/300
45/45 [==============================] - 0s 5ms/step - loss: 3.3131e-04 - val_loss: 3.1027e-04
Epoch 166/300
45/45 [==============================] - 0s 5ms/step - loss: 3.3106e-04 - val_loss: 3.0896e-04
Epoch 167/300
45/45 [==============================] - 0s 6ms/step - loss: 3.3081e-04 - val_loss: 3.0720e-04
Epoch 168/300
45/45 [==============================] - 1s 29ms/step - loss: 3.3068e-04 - val_loss: 3.0585e-04
Epoch 169/300
45/45 [==============================] - 0s 5ms/step - loss: 3.3070e-04 - val_loss: 3.0471e-04
Epoch 170/300
45/45 [==============================] - 0s 6ms/step - loss: 3.3068e-04 - val_loss: 3.0304e-04
Epoch 171/300
45/45 [==============================] - 0s 10ms/step - loss: 3.3059e-04 - val_loss: 3.0074e-04
Epoch 172/300
45/45 [==============================] - 0s 6ms/step - loss: 3.3074e-04 - val_loss: 2.9900e-04
Epoch 173/300
45/45 [==============================] - 0s 8ms/step - loss: 3.3115e-04 - val_loss: 2.9811e-04
Epoch 174/300
45/45 [==============================] - 1s 25ms/step - loss: 3.3150e-04 - val_loss: 2.9699e-04
Epoch 175/300
45/45 [==============================] - 0s 8ms/step - loss: 3.3239e-04 - val_loss: 2.9676e-04
Epoch 176/300
45/45 [==============================] - 0s 9ms/step - loss: 3.3386e-04 - val_loss: 2.9800e-04
Epoch 177/300
45/45 [==============================] - 0s 8ms/step - loss: 3.3521e-04 - val_loss: 2.9886e-04
Epoch 178/300
45/45 [==============================] - 0s 8ms/step - loss: 3.3711e-04 - val_loss: 2.9769e-04
Epoch 179/300
45/45 [==============================] - 0s 6ms/step - loss: 3.3963e-04 - val_loss: 2.9881e-04
Epoch 180/300
45/45 [==============================] - 0s 9ms/step - loss: 3.4287e-04 - val_loss: 2.9811e-04
Epoch 181/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4620e-04 - val_loss: 3.0054e-04
Epoch 182/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4865e-04 - val_loss: 2.8751e-04
Epoch 183/300
45/45 [==============================] - 0s 9ms/step - loss: 3.4994e-04 - val_loss: 2.9158e-04
Epoch 184/300
45/45 [==============================] - 0s 5ms/step - loss: 3.4764e-04 - val_loss: 2.7467e-04
Epoch 185/300
45/45 [==============================] - 1s 11ms/step - loss: 3.6259e-04 - val_loss: 2.9033e-04
Epoch 186/300
45/45 [==============================] - 0s 7ms/step - loss: 3.4826e-04 - val_loss: 2.7081e-04
Epoch 187/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5752e-04 - val_loss: 2.9569e-04
Epoch 188/300
45/45 [==============================] - 1s 15ms/step - loss: 3.3809e-04 - val_loss: 2.8537e-04
Epoch 189/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4112e-04 - val_loss: 2.9078e-04
Epoch 190/300
45/45 [==============================] - 0s 5ms/step - loss: 3.5083e-04 - val_loss: 2.8273e-04
Epoch 191/300
45/45 [==============================] - 0s 11ms/step - loss: 3.4773e-04 - val_loss: 2.8265e-04
Epoch 192/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4352e-04 - val_loss: 2.8626e-04
Epoch 193/300
45/45 [==============================] - 0s 11ms/step - loss: 3.5211e-04 - val_loss: 2.8249e-04
Epoch 194/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5232e-04 - val_loss: 2.8465e-04
Epoch 195/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4698e-04 - val_loss: 2.8851e-04
Epoch 196/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5575e-04 - val_loss: 2.7762e-04
Epoch 197/300
45/45 [==============================] - 1s 27ms/step - loss: 3.5870e-04 - val_loss: 2.8157e-04
Epoch 198/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5571e-04 - val_loss: 2.9361e-04
Epoch 199/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5478e-04 - val_loss: 2.7349e-04
Epoch 200/300
45/45 [==============================] - 0s 10ms/step - loss: 3.6051e-04 - val_loss: 2.7408e-04
Epoch 201/300
45/45 [==============================] - 1s 23ms/step - loss: 3.6632e-04 - val_loss: 2.9037e-04
Epoch 202/300
45/45 [==============================] - 0s 8ms/step - loss: 3.5670e-04 - val_loss: 2.9366e-04
Epoch 203/300
45/45 [==============================] - 0s 8ms/step - loss: 3.6676e-04 - val_loss: 2.8667e-04
Epoch 204/300
45/45 [==============================] - 2s 35ms/step - loss: 3.6919e-04 - val_loss: 2.9047e-04
Epoch 205/300
45/45 [==============================] - 0s 9ms/step - loss: 3.6637e-04 - val_loss: 2.8041e-04
Epoch 206/300
45/45 [==============================] - 1s 31ms/step - loss: 3.7311e-04 - val_loss: 2.7967e-04
Epoch 207/300
45/45 [==============================] - 0s 6ms/step - loss: 3.6358e-04 - val_loss: 2.8122e-04
Epoch 208/300
45/45 [==============================] - 0s 6ms/step - loss: 3.7808e-04 - val_loss: 2.7217e-04
Epoch 209/300
45/45 [==============================] - 1s 6ms/step - loss: 3.7394e-04 - val_loss: 2.9813e-04
Epoch 210/300
45/45 [==============================] - 0s 6ms/step - loss: 3.8183e-04 - val_loss: 2.7756e-04
Epoch 211/300
45/45 [==============================] - 0s 5ms/step - loss: 3.9591e-04 - val_loss: 2.9705e-04
Epoch 212/300
45/45 [==============================] - 1s 27ms/step - loss: 3.7749e-04 - val_loss: 2.7270e-04
Epoch 213/300
45/45 [==============================] - 0s 6ms/step - loss: 4.2069e-04 - val_loss: 2.7056e-04
Epoch 214/300
45/45 [==============================] - 0s 6ms/step - loss: 3.7402e-04 - val_loss: 2.8973e-04
Epoch 215/300
45/45 [==============================] - 0s 9ms/step - loss: 4.0269e-04 - val_loss: 2.6386e-04
Epoch 216/300
45/45 [==============================] - 1s 21ms/step - loss: 4.2226e-04 - val_loss: 2.7785e-04
Epoch 217/300
45/45 [==============================] - 0s 6ms/step - loss: 4.1082e-04 - val_loss: 2.6493e-04
Epoch 218/300
45/45 [==============================] - 0s 6ms/step - loss: 4.6506e-04 - val_loss: 2.7362e-04
Epoch 219/300
45/45 [==============================] - 0s 6ms/step - loss: 3.9874e-04 - val_loss: 2.8242e-04
Epoch 220/300
45/45 [==============================] - 1s 29ms/step - loss: 4.6143e-04 - val_loss: 2.6421e-04
Epoch 221/300
45/45 [==============================] - 1s 20ms/step - loss: 4.4364e-04 - val_loss: 2.7188e-04
Epoch 222/300
45/45 [==============================] - 0s 6ms/step - loss: 4.4733e-04 - val_loss: 2.9586e-04
Epoch 223/300
45/45 [==============================] - 0s 6ms/step - loss: 5.0921e-04 - val_loss: 2.6187e-04
Epoch 224/300
45/45 [==============================] - 0s 6ms/step - loss: 4.3734e-04 - val_loss: 2.6701e-04
Epoch 225/300
45/45 [==============================] - 1s 31ms/step - loss: 4.8979e-04 - val_loss: 2.7821e-04
Epoch 226/300
45/45 [==============================] - 0s 7ms/step - loss: 4.5046e-04 - val_loss: 2.8843e-04
Epoch 227/300
45/45 [==============================] - 0s 7ms/step - loss: 4.8540e-04 - val_loss: 2.6487e-04
Epoch 228/300
45/45 [==============================] - 1s 25ms/step - loss: 4.5098e-04 - val_loss: 2.9202e-04
Epoch 229/300
45/45 [==============================] - 0s 6ms/step - loss: 4.6231e-04 - val_loss: 2.6830e-04
Epoch 230/300
45/45 [==============================] - 0s 7ms/step - loss: 4.2928e-04 - val_loss: 2.8589e-04
Epoch 231/300
45/45 [==============================] - 0s 10ms/step - loss: 4.2724e-04 - val_loss: 2.9278e-04
Epoch 232/300
45/45 [==============================] - 0s 6ms/step - loss: 3.9638e-04 - val_loss: 2.7914e-04
Epoch 233/300
45/45 [==============================] - 0s 6ms/step - loss: 3.7710e-04 - val_loss: 2.6801e-04
Epoch 234/300
45/45 [==============================] - 0s 6ms/step - loss: 3.7149e-04 - val_loss: 2.6288e-04
Epoch 235/300
45/45 [==============================] - 1s 26ms/step - loss: 3.6775e-04 - val_loss: 2.6259e-04
Epoch 236/300
45/45 [==============================] - 0s 5ms/step - loss: 3.6465e-04 - val_loss: 2.6299e-04
Epoch 237/300
45/45 [==============================] - 0s 7ms/step - loss: 3.6324e-04 - val_loss: 2.6281e-04
Epoch 238/300
45/45 [==============================] - 0s 9ms/step - loss: 3.6222e-04 - val_loss: 2.6251e-04
Epoch 239/300
45/45 [==============================] - 1s 6ms/step - loss: 3.6135e-04 - val_loss: 2.6233e-04
Epoch 240/300
45/45 [==============================] - 1s 21ms/step - loss: 3.6055e-04 - val_loss: 2.6222e-04
Epoch 241/300
45/45 [==============================] - 1s 22ms/step - loss: 3.5984e-04 - val_loss: 2.6212e-04
Epoch 242/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5920e-04 - val_loss: 2.6205e-04
Epoch 243/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5861e-04 - val_loss: 2.6199e-04
Epoch 244/300
45/45 [==============================] - 1s 12ms/step - loss: 3.5807e-04 - val_loss: 2.6194e-04
Epoch 245/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5756e-04 - val_loss: 2.6190e-04
Epoch 246/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5708e-04 - val_loss: 2.6187e-04
Epoch 247/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5663e-04 - val_loss: 2.6185e-04
Epoch 248/300
45/45 [==============================] - 0s 10ms/step - loss: 3.5620e-04 - val_loss: 2.6183e-04
Epoch 249/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5580e-04 - val_loss: 2.6182e-04
Epoch 250/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5541e-04 - val_loss: 2.6181e-04
Epoch 251/300
45/45 [==============================] - 0s 7ms/step - loss: 3.5504e-04 - val_loss: 2.6180e-04
Epoch 252/300
45/45 [==============================] - 0s 8ms/step - loss: 3.5469e-04 - val_loss: 2.6180e-04
Epoch 253/300
45/45 [==============================] - 1s 13ms/step - loss: 3.5434e-04 - val_loss: 2.6180e-04
Epoch 254/300
45/45 [==============================] - 0s 8ms/step - loss: 3.5402e-04 - val_loss: 2.6180e-04
Epoch 255/300
45/45 [==============================] - 1s 13ms/step - loss: 3.5370e-04 - val_loss: 2.6180e-04
Epoch 256/300
45/45 [==============================] - 0s 7ms/step - loss: 3.5340e-04 - val_loss: 2.6181e-04
Epoch 257/300
45/45 [==============================] - 1s 12ms/step - loss: 3.5311e-04 - val_loss: 2.6182e-04
Epoch 258/300
45/45 [==============================] - 0s 8ms/step - loss: 3.5282e-04 - val_loss: 2.6183e-04
Epoch 259/300
45/45 [==============================] - 1s 13ms/step - loss: 3.5255e-04 - val_loss: 2.6184e-04
Epoch 260/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5229e-04 - val_loss: 2.6185e-04
Epoch 261/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5203e-04 - val_loss: 2.6186e-04
Epoch 262/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5178e-04 - val_loss: 2.6188e-04
Epoch 263/300
45/45 [==============================] - 1s 10ms/step - loss: 3.5155e-04 - val_loss: 2.6189e-04
Epoch 264/300
45/45 [==============================] - 0s 5ms/step - loss: 3.5131e-04 - val_loss: 2.6191e-04
Epoch 265/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5109e-04 - val_loss: 2.6192e-04
Epoch 266/300
45/45 [==============================] - 0s 9ms/step - loss: 3.5088e-04 - val_loss: 2.6194e-04
Epoch 267/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5067e-04 - val_loss: 2.6196e-04
Epoch 268/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5047e-04 - val_loss: 2.6197e-04
Epoch 269/300
45/45 [==============================] - 0s 6ms/step - loss: 3.5027e-04 - val_loss: 2.6199e-04
Epoch 270/300
45/45 [==============================] - 2s 44ms/step - loss: 3.5008e-04 - val_loss: 2.6201e-04
Epoch 271/300
45/45 [==============================] - 1s 22ms/step - loss: 3.4990e-04 - val_loss: 2.6203e-04
Epoch 272/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4973e-04 - val_loss: 2.6205e-04
Epoch 273/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4956e-04 - val_loss: 2.6207e-04
Epoch 274/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4939e-04 - val_loss: 2.6209e-04
Epoch 275/300
45/45 [==============================] - 1s 25ms/step - loss: 3.4924e-04 - val_loss: 2.6211e-04
Epoch 276/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4909e-04 - val_loss: 2.6212e-04
Epoch 277/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4894e-04 - val_loss: 2.6214e-04
Epoch 278/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4880e-04 - val_loss: 2.6216e-04
Epoch 279/300
45/45 [==============================] - 1s 25ms/step - loss: 3.4867e-04 - val_loss: 2.6218e-04
Epoch 280/300
45/45 [==============================] - 1s 30ms/step - loss: 3.4854e-04 - val_loss: 2.6219e-04
Epoch 281/300
45/45 [==============================] - 0s 9ms/step - loss: 3.4841e-04 - val_loss: 2.6221e-04
Epoch 282/300
45/45 [==============================] - 0s 9ms/step - loss: 3.4829e-04 - val_loss: 2.6222e-04
Epoch 283/300
45/45 [==============================] - 0s 10ms/step - loss: 3.4818e-04 - val_loss: 2.6223e-04
Epoch 284/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4807e-04 - val_loss: 2.6225e-04
Epoch 285/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4797e-04 - val_loss: 2.6226e-04
Epoch 286/300
45/45 [==============================] - 0s 9ms/step - loss: 3.4787e-04 - val_loss: 2.6227e-04
Epoch 287/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4777e-04 - val_loss: 2.6228e-04
Epoch 288/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4768e-04 - val_loss: 2.6229e-04
Epoch 289/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4760e-04 - val_loss: 2.6230e-04
Epoch 290/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4751e-04 - val_loss: 2.6230e-04
Epoch 291/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4744e-04 - val_loss: 2.6231e-04
Epoch 292/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4736e-04 - val_loss: 2.6231e-04
Epoch 293/300
45/45 [==============================] - 0s 10ms/step - loss: 3.4730e-04 - val_loss: 2.6231e-04
Epoch 294/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4723e-04 - val_loss: 2.6232e-04
Epoch 295/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4717e-04 - val_loss: 2.6232e-04
Epoch 296/300
45/45 [==============================] - 0s 5ms/step - loss: 3.4711e-04 - val_loss: 2.6232e-04
Epoch 297/300
45/45 [==============================] - 0s 9ms/step - loss: 3.4706e-04 - val_loss: 2.6232e-04
Epoch 298/300
45/45 [==============================] - 0s 6ms/step - loss: 3.4701e-04 - val_loss: 2.6233e-04
Epoch 299/300
45/45 [==============================] - 0s 5ms/step - loss: 3.4698e-04 - val_loss: 2.6232e-04
Epoch 300/300
45/45 [==============================] - 0s 10ms/step - loss: 3.4695e-04 - val_loss: 2.6234e-04
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm (LSTM) (None, 256) 264192
dense (Dense) (None, 1) 257
=================================================================
Total params: 264449 (1.01 MB)
Trainable params: 264449 (1.01 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
In [ ]:
# Obtener las predicciones en los conjuntos de entrenamiento y prueba
plt.figure(figsize=(12,8))
Xt = model.predict(X_train)
plt.plot(scl.inverse_transform(y_train.reshape(-1,1)), label="Actual")
plt.plot(scl.inverse_transform(Xt), label="Predicted")
plt.legend()
plt.title("Train Dataset")
45/45 [==============================] - 6s 88ms/step
Out[ ]:
Text(0.5, 1.0, 'Train Dataset')
In [ ]:
plt.figure(figsize=(12,8))
Xt = model.predict(X_test)
plt.plot(scl.inverse_transform(y_test.reshape(-1,1)), label="Actual")
plt.plot(scl.inverse_transform(Xt), label="Predicted")
plt.legend()
plt.title("Test Dataset")
5/5 [==============================] - 0s 7ms/step
Out[ ]:
Text(0.5, 1.0, 'Test Dataset')
In [ ]:
# Crear una función para procesar los datos en segmentos de 10 observaciones sin etiquetas
def processData(data, lb):
X = []
for i in range(len(data)-lb-1):
X.append(data[i:(i+lb), 0])
return np.array(X)
In [ ]:
# Crear un conjunto de datos ficticio con los valores predichos como características
cl2 = cl.copy()
pred = []
for i in range(X_test.shape[0]):
cl2[int(X.shape[0]*0.90)+i+lb] = model.predict(X_test)[i]
pred.extend(model.predict(X_test)[i])
X = processData(cl2, lb)
X_train, X_test = X[:int(X.shape[0]*0.90)], X[int(X.shape[0]*0.90):]
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
5/5 [==============================] - 1s 177ms/step 5/5 [==============================] - 0s 78ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 2s 467ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 1s 150ms/step 5/5 [==============================] - 0s 60ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 65ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 1s 184ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 58ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 1s 171ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 44ms/step 5/5 [==============================] - 1s 172ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 1s 206ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 1s 197ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 1s 195ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 34ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 1s 218ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 1s 176ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 41ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 47ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 50ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 42ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 6ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 6ms/step 5/5 [==============================] - 1s 193ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 1s 130ms/step 5/5 [==============================] - 0s 44ms/step 5/5 [==============================] - 1s 157ms/step 5/5 [==============================] - 0s 70ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 1s 186ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 5ms/step 5/5 [==============================] - 1s 222ms/step 5/5 [==============================] - 0s 119ms/step 5/5 [==============================] - 0s 24ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 1s 159ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 1s 216ms/step 5/5 [==============================] - 1s 179ms/step 5/5 [==============================] - 0s 82ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 48ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 1s 157ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 1s 151ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 1s 179ms/step 5/5 [==============================] - 0s 89ms/step 5/5 [==============================] - 1s 165ms/step 5/5 [==============================] - 0s 74ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 6ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 1s 183ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 1s 181ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 80ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 1s 210ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 112ms/step 5/5 [==============================] - 0s 27ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 3ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 7ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 6ms/step 5/5 [==============================] - 1s 185ms/step 5/5 [==============================] - 0s 61ms/step 5/5 [==============================] - 0s 6ms/step 5/5 [==============================] - 0s 4ms/step 5/5 [==============================] - 0s 6ms/step 5/5 [==============================] - 0s 5ms/step
In [ ]:
# Obtener las predicciones para 251 observaciones adelante
prediction = scl.inverse_transform(np.array(pred).reshape(-1, 1))
plt.figure(figsize=(12,8))
plt.plot(scl.inverse_transform(y_test.reshape(-1,1)), label="Actual")
plt.plot(prediction, label="Predicted")
plt.title("Test Dataset 251 Obs Ahead")
Out[ ]:
Text(0.5, 1.0, 'Test Dataset 251 Obs Ahead')
In [ ]: