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Newnius
2020-05-02 09:47:26 +08:00
parent a12bd1a7a3
commit d0d9e3f5bc
2 changed files with 34 additions and 36 deletions
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45
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25
train.py
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@@ -12,6 +12,11 @@ from math import sqrt
import numpy import numpy
# date-time parsing function for loading the dataset
def parser(x):
return datetime.strptime('190' + x, '%Y-%m')
# frame a sequence as a supervised learning problem # frame a sequence as a supervised learning problem
def timeseries_to_supervised(data, lag=1): def timeseries_to_supervised(data, lag=1):
df = DataFrame(data) df = DataFrame(data)
@@ -76,13 +81,11 @@ def fit_lstm(train, batch_size, nb_epoch, neurons):
# make a one-step forecast # make a one-step forecast
def forecast_lstm(model, batch_size, X): def forecast_lstm(model, batch_size, X):
X = X.reshape(X.shape[0], X.shape[1], len(X)) X = X.reshape(1, 1, len(X))
yhat = model.predict(X, batch_size=batch_size) yhat = model.predict(X, batch_size=batch_size)
return yhat[0, 0] return yhat[0, 0]
batch_size = 12
# load dataset # load dataset
series = read_csv('data.csv', header=0, index_col=0, squeeze=True) series = read_csv('data.csv', header=0, index_col=0, squeeze=True)
@@ -101,25 +104,19 @@ train, test = supervised_values[0:-12], supervised_values[-12:]
scaler, train_scaled, test_scaled = scale(train, test) scaler, train_scaled, test_scaled = scale(train, test)
# fit the model # fit the model
lstm_model = fit_lstm(train_scaled, batch_size, 30, 4) lstm_model = fit_lstm(train_scaled, 1, 30, 4)
# forecast the entire training dataset to build up state for forecasting # forecast the entire training dataset to build up state for forecasting
train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1) train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)
lstm_model.predict(train_reshaped, batch_size=batch_size) lstm_model.predict(train_reshaped, batch_size=1)
print(raw_values)
print(diff_values)
print(supervised_values)
print(train, test)
print(train_scaled, test_scaled)
# walk-forward validation on the test data # walk-forward validation on the test data
predictions = list() predictions = list()
for j in range(len(test_scaled)): for j in range(len(test_scaled)):
# make one-step forecast # make one-step forecast
#X, y = test_scaled[j, 0:-1], test_scaled[j, -1] X, y = test_scaled[j, 0:-1], test_scaled[j, -1]
yhat = forecast_lstm(lstm_model, batch_size, test_scaled) yhat = forecast_lstm(lstm_model, 1, X)
# invert scaling # invert scaling
yhat = invert_scale(scaler, test_scaled, yhat) yhat = invert_scale(scaler, X, yhat)
# invert differencing # invert differencing
yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - j) yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - j)
# store forecast # store forecast