update

train.py

@@ -12,6 +12,11 @@ from math import sqrt
 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
 def timeseries_to_supervised(data, lag=1):
 	df = DataFrame(data)
@@ -76,13 +81,11 @@ def fit_lstm(train, batch_size, nb_epoch, neurons):
 
 # make a one-step forecast
 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)
 	return yhat[0, 0]
 
 
-batch_size = 12
-
 # load dataset
 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)
 
 # 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
 train_reshaped = train_scaled[:, 0].reshape(len(train_scaled), 1, 1)
-lstm_model.predict(train_reshaped, batch_size=batch_size)
-
-print(raw_values)
-print(diff_values)
-print(supervised_values)
-print(train, test)
-print(train_scaled, test_scaled)
+lstm_model.predict(train_reshaped, batch_size=1)
 
 # walk-forward validation on the test data
 predictions = list()
 for j in range(len(test_scaled)):
 	# make one-step forecast
-	#X, y = test_scaled[j, 0:-1], test_scaled[j, -1]
-	yhat = forecast_lstm(lstm_model, batch_size, test_scaled)
+	X, y = test_scaled[j, 0:-1], test_scaled[j, -1]
+	yhat = forecast_lstm(lstm_model, 1, X)
 	# invert scaling
-	yhat = invert_scale(scaler, test_scaled, yhat)
+	yhat = invert_scale(scaler, X, yhat)
 	# invert differencing
 	yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - j)
 	# store forecast