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