LSTM

import numpy
import matplotlib.pyplot as plt
from pandas import read_csv
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back=1):
	dataX, dataY = [], []
	for i in range(len(dataset)-look_back-1):
		a = dataset[i:(i+look_back), 0]
		dataX.append(a)
		dataY.append(dataset[i + look_back, 0])
	return numpy.array(dataX), numpy.array(dataY)
# fix random seed for reproducibility
numpy.random.seed(7)
# load the dataset
dataframe = read_csv('ai_data_test8.csv', usecols=[1], engine='python', skipfooter=2)
dataset = dataframe.values
dataset = dataset.astype('float32')
# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
# split into train and test sets
train_size = int(len(dataset) - 4)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
# reshape into X=t and Y=t+1
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))

# create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=8, batch_size=5, verbose=2)
# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# invert predictions
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# calculate root mean squared error
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))
# shift train predictions for plotting
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
# shift test predictions for plotting
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict


# plot baseline and predictions
rounded = [round(x[0]) for x in testPredict]
print(rounded)
#expected = [round(x[0]) for x in testY]
print(testY)
print(testY.shape)

#plt.plot(scaler.inverse_transform(dataset))
#plt.plot(trainPredictPlot)
#plt.plot(testX)
#plt.plot(testPredictPlot)
#plt.show()



#plt.plot(dataset, color='red')
#plt.show()
#plt.plot(testX, color='blue')
#plt.plot(testPredict, color='red')

#plt.xlabel('hours')
#plt.legend(['predicted', 'expected'], loc='upper left')
#plt.plot('accuracy', )
#plt.scatter(trainX,testX)
#plt.show()

#plt.plot(accuracy)
#plt.show()

plt.plot(testY, color='blue')
plt.plot(testPredictPlot, color='green')

plt.title('LSTM RNN for Forecasting Load Demand')
plt.ylabel('Load Demand (Megawatts)')
plt.xlabel('Hours')
plt.legend(['Expected', 'Predicted'], loc='upper left')
plt.show()

plt.plot(scaler.inverse_transform(dataset), color='orange')
plt.plot(trainPredictPlot, color= 'blue')
plt.plot(testPredictPlot, color='green')

plt.title('Training and Testing Prediction results')
plt.ylabel('Load Demand (Megawatts)')
plt.xlabel('Hours')
plt.legend(['Actual Data', 'Training Data', 'Test Data'], loc='upper left')
plt.show()