# Predicción de Series Temporales NN - Multivariate¶

El artículo completo con la explicación detallada en el blog: http://www.aprendemachinelearning.com/

## Usaremos Keras y Tensorflow¶

Importamos las Librerías que vamos a utilizar

In [161]:
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (16, 9)
plt.style.use('fast')

from keras.models import Sequential
from keras.layers import Dense,Activation,Flatten
from sklearn.preprocessing import MinMaxScaler


### Cargamos nuestro Dataset¶

In [162]:
df = pd.read_csv('time_series.csv',  parse_dates=[0], header=None,index_col=0, names=['fecha','unidades'])

Out[162]:
fecha
2017-01-02 236
2017-01-03 237
2017-01-04 290
2017-01-05 221
2017-01-07 128

### Cargamos Datos Categóricos: Día y Mes¶

In [163]:
df['weekday']=[x.weekday() for x in df.index]
df['month']=[x.month for x in df.index]

Out[163]:
fecha
2017-01-02 236 0 1
2017-01-03 237 1 1
2017-01-04 290 2 1
2017-01-05 221 3 1
2017-01-07 128 5 1
In [164]:
df.describe()

Out[164]:
count 604.000000 604.000000 604.000000
mean 215.935430 2.644040 6.304636
std 75.050304 1.818674 3.312359
min 51.000000 0.000000 1.000000
25% 171.000000 1.000000 3.000000
50% 214.000000 3.000000 6.000000
75% 261.250000 4.000000 9.000000
max 591.000000 6.000000 12.000000

In [ ]:
# convert series to supervised learning
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
n_vars = 1 if type(data) is list else data.shape[1]
df = pd.DataFrame(data)
cols, names = list(), list()
# input sequence (t-n, ... t-1)
for i in range(n_in, 0, -1):
cols.append(df.shift(i))
names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
# forecast sequence (t, t+1, ... t+n)
for i in range(0, n_out):
cols.append(df.shift(-i))
if i == 0:
names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
else:
names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
# put it all together
agg = pd.concat(cols, axis=1)
agg.columns = names
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg


In [165]:
PASOS=7

# ensure all data is float
values = values.astype('float32')
# normalize features
scaler = MinMaxScaler(feature_range=(-1, 1))
values=values.reshape(-1, 1) # esto lo hacemos porque tenemos 1 sola dimension
scaled = scaler.fit_transform(values)

df['scaled'] = scaled
#print(scaledMerge.values)

# frame as supervised learning
reframed = series_to_supervised(scaledMerge, PASOS, 1)

[[ 0.          1.         -0.31481487]
[ 1.          1.         -0.31111115]
[ 2.          1.         -0.11481488]
...
[ 2.         11.         -0.51111114]
[ 3.         11.         -0.25925928]
[ 4.         11.         -0.48888892]]

Out[165]:
var1(t-7) var2(t-7) var3(t-7) var1(t-6) var2(t-6) var3(t-6) var1(t-5) var2(t-5) var3(t-5) var1(t-4) ... var3(t-3) var1(t-2) var2(t-2) var3(t-2) var1(t-1) var2(t-1) var3(t-1) var1(t) var2(t) var3(t)
fecha
2017-01-11 0.0 1.0 -0.314815 1.0 1.0 -0.311111 2.0 1.0 -0.114815 3.0 ... -0.714815 0.0 1.0 -0.103704 1.0 1.0 -0.225926 2 1 -0.433333
2017-01-12 1.0 1.0 -0.311111 2.0 1.0 -0.114815 3.0 1.0 -0.370370 5.0 ... -0.103704 1.0 1.0 -0.225926 2.0 1.0 -0.433333 3 1 -0.607407
2017-01-13 2.0 1.0 -0.114815 3.0 1.0 -0.370370 5.0 1.0 -0.714815 0.0 ... -0.225926 2.0 1.0 -0.433333 3.0 1.0 -0.607407 4 1 -0.522222
2017-01-14 3.0 1.0 -0.370370 5.0 1.0 -0.714815 0.0 1.0 -0.103704 1.0 ... -0.433333 3.0 1.0 -0.607407 4.0 1.0 -0.522222 5 1 -0.644444
2017-01-16 5.0 1.0 -0.714815 0.0 1.0 -0.103704 1.0 1.0 -0.225926 2.0 ... -0.607407 4.0 1.0 -0.522222 5.0 1.0 -0.644444 0 1 -0.344444

5 rows × 24 columns

## Dividimos en set de Entrenamiento y Validación¶

In [166]:
newReframed=reframed.drop(['var1(t)','var2(t)'],axis=1)
print(newReframed.shape)

(597, 22)

Out[166]:
var1(t-7) var2(t-7) var3(t-7) var1(t-6) var2(t-6) var3(t-6) var1(t-5) var2(t-5) var3(t-5) var1(t-4) ... var1(t-3) var2(t-3) var3(t-3) var1(t-2) var2(t-2) var3(t-2) var1(t-1) var2(t-1) var3(t-1) var3(t)
fecha
2017-01-11 0.0 1.0 -0.314815 1.0 1.0 -0.311111 2.0 1.0 -0.114815 3.0 ... 5.0 1.0 -0.714815 0.0 1.0 -0.103704 1.0 1.0 -0.225926 -0.433333
2017-01-12 1.0 1.0 -0.311111 2.0 1.0 -0.114815 3.0 1.0 -0.370370 5.0 ... 0.0 1.0 -0.103704 1.0 1.0 -0.225926 2.0 1.0 -0.433333 -0.607407
2017-01-13 2.0 1.0 -0.114815 3.0 1.0 -0.370370 5.0 1.0 -0.714815 0.0 ... 1.0 1.0 -0.225926 2.0 1.0 -0.433333 3.0 1.0 -0.607407 -0.522222
2017-01-14 3.0 1.0 -0.370370 5.0 1.0 -0.714815 0.0 1.0 -0.103704 1.0 ... 2.0 1.0 -0.433333 3.0 1.0 -0.607407 4.0 1.0 -0.522222 -0.644444
2017-01-16 5.0 1.0 -0.714815 0.0 1.0 -0.103704 1.0 1.0 -0.225926 2.0 ... 3.0 1.0 -0.607407 4.0 1.0 -0.522222 5.0 1.0 -0.644444 -0.344444

5 rows × 22 columns

In [167]:
# split into train and test sets
values = newReframed.values
n_train_days = 315+289 - (30+PASOS)
train = values[:n_train_days, :]
test = values[n_train_days:, :]
# split into input and outputs
x_train, y_train = train[:, :-1], train[:, -1]
x_val, y_val = test[:, :-1], test[:, -1]
# reshape input to be 3D [samples, timesteps, features]
x_train = x_train.reshape((x_train.shape[0], 1, x_train.shape[1]))
x_val = x_val.reshape((x_val.shape[0], 1, x_val.shape[1]))
print(x_train.shape, y_train.shape, x_val.shape, y_val.shape)

(567, 1, 21) (567,) (30, 1, 21) (30,)


# Creamos el Modelo de Red Neuronal¶

## Utilizaremos una Red Feedforward¶

### Como entradas son 21 columnas (7 pasos por 3 variables)¶

In [168]:
def crear_modeloFF():
model = Sequential()
model.summary()
return model


## Entrenamos nuestra máquina¶

In [169]:
EPOCHS=40

model = crear_modeloFF()

history=model.fit(x_train,y_train,epochs=EPOCHS,validation_data=(x_val,y_val),batch_size=PASOS)

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense_9 (Dense)              (None, 1, 7)              154
_________________________________________________________________
flatten_5 (Flatten)          (None, 7)                 0
_________________________________________________________________
dense_10 (Dense)             (None, 1)                 8
=================================================================
Total params: 162
Trainable params: 162
Non-trainable params: 0
_________________________________________________________________
Train on 567 samples, validate on 30 samples
Epoch 1/40
567/567 [==============================] - 0s 769us/step - loss: 0.9168 - mean_squared_error: 1.0901 - val_loss: 0.2777 - val_mean_squared_error: 0.1117
Epoch 2/40
567/567 [==============================] - 0s 232us/step - loss: 0.3147 - mean_squared_error: 0.1677 - val_loss: 0.2270 - val_mean_squared_error: 0.0790
Epoch 3/40
567/567 [==============================] - 0s 229us/step - loss: 0.2561 - mean_squared_error: 0.1173 - val_loss: 0.2822 - val_mean_squared_error: 0.1181
Epoch 4/40
567/567 [==============================] - 0s 222us/step - loss: 0.2370 - mean_squared_error: 0.0952 - val_loss: 0.2239 - val_mean_squared_error: 0.0770
Epoch 5/40
567/567 [==============================] - 0s 212us/step - loss: 0.2175 - mean_squared_error: 0.0842 - val_loss: 0.1679 - val_mean_squared_error: 0.0480
Epoch 6/40
567/567 [==============================] - 0s 214us/step - loss: 0.2056 - mean_squared_error: 0.0778 - val_loss: 0.1868 - val_mean_squared_error: 0.0543
Epoch 7/40
567/567 [==============================] - 0s 218us/step - loss: 0.1965 - mean_squared_error: 0.0724 - val_loss: 0.1976 - val_mean_squared_error: 0.0596
Epoch 8/40
567/567 [==============================] - 0s 226us/step - loss: 0.1883 - mean_squared_error: 0.0699 - val_loss: 0.1467 - val_mean_squared_error: 0.0378
Epoch 9/40
567/567 [==============================] - 0s 213us/step - loss: 0.1789 - mean_squared_error: 0.0662 - val_loss: 0.1635 - val_mean_squared_error: 0.0477
Epoch 10/40
567/567 [==============================] - 0s 222us/step - loss: 0.1719 - mean_squared_error: 0.0648 - val_loss: 0.1554 - val_mean_squared_error: 0.0388
Epoch 11/40
567/567 [==============================] - 0s 217us/step - loss: 0.1697 - mean_squared_error: 0.0620 - val_loss: 0.1667 - val_mean_squared_error: 0.0464
Epoch 12/40
567/567 [==============================] - 0s 210us/step - loss: 0.1661 - mean_squared_error: 0.0607 - val_loss: 0.1357 - val_mean_squared_error: 0.0354
Epoch 13/40
567/567 [==============================] - 0s 217us/step - loss: 0.1635 - mean_squared_error: 0.0601 - val_loss: 0.1529 - val_mean_squared_error: 0.0405
Epoch 14/40
567/567 [==============================] - 0s 212us/step - loss: 0.1641 - mean_squared_error: 0.0605 - val_loss: 0.1435 - val_mean_squared_error: 0.0399
Epoch 15/40
567/567 [==============================] - 0s 213us/step - loss: 0.1607 - mean_squared_error: 0.0591 - val_loss: 0.1301 - val_mean_squared_error: 0.0325
Epoch 16/40
567/567 [==============================] - 0s 216us/step - loss: 0.1607 - mean_squared_error: 0.0594 - val_loss: 0.1343 - val_mean_squared_error: 0.0362
Epoch 17/40
567/567 [==============================] - 0s 212us/step - loss: 0.1598 - mean_squared_error: 0.0586 - val_loss: 0.1448 - val_mean_squared_error: 0.0381
Epoch 18/40
567/567 [==============================] - 0s 212us/step - loss: 0.1597 - mean_squared_error: 0.0588 - val_loss: 0.1447 - val_mean_squared_error: 0.0391
Epoch 19/40
567/567 [==============================] - 0s 219us/step - loss: 0.1590 - mean_squared_error: 0.0580 - val_loss: 0.1495 - val_mean_squared_error: 0.0435
Epoch 20/40
567/567 [==============================] - 0s 215us/step - loss: 0.1597 - mean_squared_error: 0.0582 - val_loss: 0.1427 - val_mean_squared_error: 0.0383
Epoch 21/40
567/567 [==============================] - 0s 218us/step - loss: 0.1577 - mean_squared_error: 0.0583 - val_loss: 0.1425 - val_mean_squared_error: 0.0350
Epoch 22/40
567/567 [==============================] - 0s 213us/step - loss: 0.1576 - mean_squared_error: 0.0566 - val_loss: 0.1379 - val_mean_squared_error: 0.0376
Epoch 23/40
567/567 [==============================] - 0s 258us/step - loss: 0.1572 - mean_squared_error: 0.0567 - val_loss: 0.1393 - val_mean_squared_error: 0.0373
Epoch 24/40
567/567 [==============================] - 0s 275us/step - loss: 0.1553 - mean_squared_error: 0.0555 - val_loss: 0.1354 - val_mean_squared_error: 0.0349
Epoch 25/40
567/567 [==============================] - 0s 206us/step - loss: 0.1552 - mean_squared_error: 0.0568 - val_loss: 0.1534 - val_mean_squared_error: 0.0429
Epoch 26/40
567/567 [==============================] - 0s 201us/step - loss: 0.1544 - mean_squared_error: 0.0545 - val_loss: 0.1471 - val_mean_squared_error: 0.0386
Epoch 27/40
567/567 [==============================] - 0s 228us/step - loss: 0.1534 - mean_squared_error: 0.0539 - val_loss: 0.1517 - val_mean_squared_error: 0.0437
Epoch 28/40
567/567 [==============================] - 0s 213us/step - loss: 0.1530 - mean_squared_error: 0.0543 - val_loss: 0.1388 - val_mean_squared_error: 0.0365
Epoch 29/40
567/567 [==============================] - 0s 227us/step - loss: 0.1532 - mean_squared_error: 0.0534 - val_loss: 0.1430 - val_mean_squared_error: 0.0388
Epoch 30/40
567/567 [==============================] - 0s 239us/step - loss: 0.1515 - mean_squared_error: 0.0530 - val_loss: 0.1303 - val_mean_squared_error: 0.0314
Epoch 31/40
567/567 [==============================] - 0s 204us/step - loss: 0.1533 - mean_squared_error: 0.0544 - val_loss: 0.1339 - val_mean_squared_error: 0.0336
Epoch 32/40
567/567 [==============================] - 0s 202us/step - loss: 0.1521 - mean_squared_error: 0.0527 - val_loss: 0.1428 - val_mean_squared_error: 0.0366
Epoch 33/40
567/567 [==============================] - 0s 209us/step - loss: 0.1517 - mean_squared_error: 0.0531 - val_loss: 0.1403 - val_mean_squared_error: 0.0369
Epoch 34/40
567/567 [==============================] - 0s 235us/step - loss: 0.1505 - mean_squared_error: 0.0521 - val_loss: 0.1491 - val_mean_squared_error: 0.0394
Epoch 35/40
567/567 [==============================] - 0s 218us/step - loss: 0.1508 - mean_squared_error: 0.0524 - val_loss: 0.1441 - val_mean_squared_error: 0.0385
Epoch 36/40
567/567 [==============================] - 0s 204us/step - loss: 0.1536 - mean_squared_error: 0.0537 - val_loss: 0.1363 - val_mean_squared_error: 0.0330
Epoch 37/40
567/567 [==============================] - 0s 296us/step - loss: 0.1531 - mean_squared_error: 0.0534 - val_loss: 0.1528 - val_mean_squared_error: 0.0424
Epoch 38/40
567/567 [==============================] - 0s 277us/step - loss: 0.1507 - mean_squared_error: 0.0522 - val_loss: 0.1380 - val_mean_squared_error: 0.0348
Epoch 39/40
567/567 [==============================] - 0s 318us/step - loss: 0.1503 - mean_squared_error: 0.0532 - val_loss: 0.1398 - val_mean_squared_error: 0.0355
Epoch 40/40
567/567 [==============================] - 0s 282us/step - loss: 0.1508 - mean_squared_error: 0.0518 - val_loss: 0.1411 - val_mean_squared_error: 0.0389


In [183]:
results=model.predict(x_val)
print( len(results) )
plt.scatter(range(len(y_val)),y_val,c='g')
plt.scatter(range(len(results)),results,c='r')
plt.title('validate')
plt.show()

30

In [186]:
plt.ylim(0.12, 0.35)
plt.plot(history.history['loss'])
plt.title('loss')
plt.plot(history.history['val_loss'])
plt.title('validate loss')
plt.show()

In [191]:
plt.ylim(0.01, 0.18)
plt.title('Accuracy')
plt.plot(history.history['mean_squared_error'])
plt.show()

In [173]:
compara = pd.DataFrame(np.array([y_val, [x[0] for x in results]])).transpose()
compara.columns = ['real', 'prediccion']

inverted = scaler.inverse_transform(compara.values)

compara2 = pd.DataFrame(inverted)
compara2.columns = ['real', 'prediccion']
compara2['diferencia'] = compara2['real'] - compara2['prediccion']

Out[173]:
real prediccion diferencia
0 252.000006 292.846415 -40.846410
1 220.000002 271.207719 -51.207718
2 296.000009 250.841323 45.158686
3 64.999995 233.249007 -168.249012
4 212.999999 233.514626 -20.514627
5 95.999996 150.834429 -54.834433
6 274.999986 239.217922 35.782064
7 201.000000 198.954112 2.045887
8 165.000001 199.531724 -34.531722
9 162.999996 189.600053 -26.600057
In [174]:
compara2.describe()

Out[174]:
real prediccion diferencia
count 30.000000 30.000000 30.000000
mean 191.633332 214.917230 -23.283898
std 57.580817 35.812104 48.738560
min 64.999995 150.834429 -168.249012
25% 169.000000 194.593611 -44.651899
50% 200.499998 209.075410 -24.525737
75% 220.000002 237.060020 1.978475
max 296.000009 292.846415 45.158686
In [192]:
compara2['real'].plot()
compara2['prediccion'].plot()

Out[192]:
<matplotlib.axes._subplots.AxesSubplot at 0x1a2b57a9e8>

# Pronóstico¶

A partir de la última semana de noviembre 2018, intentaremos predecir la primer semana de diciembre.

In [150]:
ultimosDias = df['2018-11-16':'2018-11-30']
ultimosDias

Out[150]:
fecha
2018-11-16 152 4 11 -0.625926
2018-11-17 111 5 11 -0.777778
2018-11-19 207 0 11 -0.422222
2018-11-20 206 1 11 -0.425926
2018-11-21 183 2 11 -0.511111
2018-11-22 200 3 11 -0.448148
2018-11-23 187 4 11 -0.496296
2018-11-24 189 5 11 -0.488889
2018-11-25 76 6 11 -0.907407
2018-11-26 276 0 11 -0.166667
2018-11-27 220 1 11 -0.374074
2018-11-28 183 2 11 -0.511111
2018-11-29 251 3 11 -0.259259
2018-11-30 189 4 11 -0.488889

## Preparamos los datos para Test¶

In [151]:
scaledMerge=ultimosDias.drop('unidades',axis=1)
print(scaledMerge.values)

# frame as supervised learning
reframed = series_to_supervised(scaledMerge, PASOS, 1)
newReframed=reframed.drop(['var1(t)','var2(t)','var3(t)'],axis=1)

[[ 4.         11.         -0.62592596]
[ 5.         11.         -0.77777779]
[ 0.         11.         -0.42222226]
[ 1.         11.         -0.42592597]
[ 2.         11.         -0.51111114]
[ 3.         11.         -0.44814819]
[ 4.         11.         -0.49629635]
[ 5.         11.         -0.48888892]
[ 6.         11.         -0.9074074 ]
[ 0.         11.         -0.16666675]
[ 1.         11.         -0.3740741 ]
[ 2.         11.         -0.51111114]
[ 3.         11.         -0.25925928]
[ 4.         11.         -0.48888892]]

Out[151]:
var1(t-7) var2(t-7) var3(t-7) var1(t-6) var2(t-6) var3(t-6) var1(t-5) var2(t-5) var3(t-5) var1(t-4) ... var3(t-4) var1(t-3) var2(t-3) var3(t-3) var1(t-2) var2(t-2) var3(t-2) var1(t-1) var2(t-1) var3(t-1)
fecha
2018-11-24 4.0 11.0 -0.625926 5.0 11.0 -0.777778 0.0 11.0 -0.422222 1.0 ... -0.425926 2.0 11.0 -0.511111 3.0 11.0 -0.448148 4.0 11.0 -0.496296
2018-11-25 5.0 11.0 -0.777778 0.0 11.0 -0.422222 1.0 11.0 -0.425926 2.0 ... -0.511111 3.0 11.0 -0.448148 4.0 11.0 -0.496296 5.0 11.0 -0.488889
2018-11-26 0.0 11.0 -0.422222 1.0 11.0 -0.425926 2.0 11.0 -0.511111 3.0 ... -0.448148 4.0 11.0 -0.496296 5.0 11.0 -0.488889 6.0 11.0 -0.907407
2018-11-27 1.0 11.0 -0.425926 2.0 11.0 -0.511111 3.0 11.0 -0.448148 4.0 ... -0.496296 5.0 11.0 -0.488889 6.0 11.0 -0.907407 0.0 11.0 -0.166667
2018-11-28 2.0 11.0 -0.511111 3.0 11.0 -0.448148 4.0 11.0 -0.496296 5.0 ... -0.488889 6.0 11.0 -0.907407 0.0 11.0 -0.166667 1.0 11.0 -0.374074
2018-11-29 3.0 11.0 -0.448148 4.0 11.0 -0.496296 5.0 11.0 -0.488889 6.0 ... -0.907407 0.0 11.0 -0.166667 1.0 11.0 -0.374074 2.0 11.0 -0.511111
2018-11-30 4.0 11.0 -0.496296 5.0 11.0 -0.488889 6.0 11.0 -0.907407 0.0 ... -0.166667 1.0 11.0 -0.374074 2.0 11.0 -0.511111 3.0 11.0 -0.259259

7 rows × 21 columns

In [153]:
values = newReframed.values
x_test = values[6:, :]
x_test = x_test.reshape((x_test.shape[0], 1, x_test.shape[1]))
print(x_test.shape)
print(x_test)
ultDiaSemana = newReframed.index[len(newReframed.index)-1].weekday()

(1, 1, 21)
[[[ 4.         11.         -0.49629635  5.         11.
-0.48888892  6.         11.         -0.9074074   0.
11.         -0.16666675  1.         11.         -0.3740741
2.         11.         -0.51111114  3.         11.
-0.25925928]]]

In [154]:
def agregarNuevoValor(x_test,nuevoValor,ultDiaSemana):
for i in range(x_test.shape[2]-3):
x_test[0][0][i] = x_test[0][0][i+3]
ultDiaSemana=ultDiaSemana+1
if ultDiaSemana>6:
ultDiaSemana=0
x_test[0][0][x_test.shape[2]-3]=ultDiaSemana
x_test[0][0][x_test.shape[2]-2]=12
x_test[0][0][x_test.shape[2]-1]=nuevoValor
return x_test,ultDiaSemana


## Pronóstico para la "próxima semana"¶

In [155]:
results=[]
for i in range(7):
parcial=model.predict(x_test)
results.append(parcial[0])
print('pred',i,x_test)
x_test,ultDiaSemana=agregarNuevoValor(x_test,parcial[0],ultDiaSemana)


pred 0 [[[ 4.         11.         -0.49629635  5.         11.
-0.48888892  6.         11.         -0.9074074   0.
11.         -0.16666675  1.         11.         -0.3740741
2.         11.         -0.51111114  3.         11.
-0.25925928]]]
pred 1 [[[ 5.         11.         -0.48888892  6.         11.
-0.9074074   0.         11.         -0.16666675  1.
11.         -0.3740741   2.         11.         -0.51111114
3.         11.         -0.25925928  5.         12.
-0.30600056]]]
pred 2 [[[ 6.         11.         -0.9074074   0.         11.
-0.16666675  1.         11.         -0.3740741   2.
11.         -0.51111114  3.         11.         -0.25925928
5.         12.         -0.30600056  6.         12.
-0.60506994]]]
pred 3 [[[ 0.         11.         -0.16666675  1.         11.
-0.3740741   2.         11.         -0.51111114  3.
11.         -0.25925928  5.         12.         -0.30600056
6.         12.         -0.60506994  0.         12.
-0.16298515]]]
pred 4 [[[ 1.         11.         -0.3740741   2.         11.
-0.51111114  3.         11.         -0.25925928  5.
12.         -0.30600056  6.         12.         -0.60506994
0.         12.         -0.16298515  1.         12.
-0.31580934]]]
pred 5 [[[ 2.         11.         -0.51111114  3.         11.
-0.25925928  5.         12.         -0.30600056  6.
12.         -0.60506994  0.         12.         -0.16298515
1.         12.         -0.31580934  2.         12.
-0.35640854]]]
pred 6 [[[ 3.         11.         -0.25925928  5.         12.
-0.30600056  6.         12.         -0.60506994  0.
12.         -0.16298515  1.         12.         -0.31580934
2.         12.         -0.35640854  3.         12.
-0.22789632]]]


In [156]:
adimen = [x for x in results]
inverted

[array([-0.30600056], dtype=float32), array([-0.60506994], dtype=float32), array([-0.16298515], dtype=float32), array([-0.31580934], dtype=float32), array([-0.35640854], dtype=float32), array([-0.22789632], dtype=float32), array([-0.21720925], dtype=float32)]

Out[156]:
array([[238.37985788],
[157.63112526],
[276.99402048],
[235.73148757],
[224.76970415],
[259.46800378],
[262.35351222]])

## Visualizamos el pronóstico¶

In [157]:
prediccion1SemanaDiciembre = pd.DataFrame(inverted)

In [158]:
prediccion1SemanaDiciembre

Out[158]:
pronostico
0 238.379858
1 157.631125
2 276.994020
3 235.731488
4 224.769704
5 259.468004
6 262.353512

El artículo completo en www.aprendemachinelearning.com