time_series_forecasting.py

# https://datamarket.com/data/set/22ox/monthly-milk-production-pounds-per-cow-jan-62-dec-75#!ds=22ox&display=line
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense

from tcn import TCN

##
# It's a very naive (toy) example to show how to do time series forecasting.
# - There are no training-testing sets here. Everything is training set for simplicity.
# - There is no input/output normalization.
# - The model is simple.
##

milk = pd.read_csv('monthly-milk-production-pounds-p.csv', index_col=0, parse_dates=True)

print(milk.head())

lookback_window = 12  # months.

milk = milk.values  # just keep np array here for simplicity.

x, y = [], []
for i in range(lookback_window, len(milk)):
    x.append(milk[i - lookback_window:i])
    y.append(milk[i])
x = np.array(x)
y = np.array(y)

print(x.shape)
print(y.shape)

# noinspection PyArgumentEqualDefault
model = Sequential([
    TCN(input_shape=(lookback_window, 1),
        kernel_size=2,
        use_skip_connections=False,
        use_batch_norm=False,
        use_weight_norm=False,
        use_layer_norm=False
        ),
    Dense(1, activation='linear')
])

model.summary()
model.compile('adam', 'mae')

print('Train...')
model.fit(x, y, epochs=100, verbose=2)

p = model.predict(x)

plt.plot(p)
plt.plot(y)
plt.title('Monthly Milk Production (in pounds)')
plt.legend(['predicted', 'actual'])
plt.show()