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| import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import LSTM, Dense
import numpy as np
import matplotlib.pyplot as plt
filepath = '/data810_2.xlsx'
data = pd.read_excel(filepath)
data['Time'] = pd.to_datetime(data['Time'], format='%Y-%m-%d-%H.%M.%S.%f')
data = data.sort_values('Time')
values = data['Data'].values.reshape(-1, 1)
scaler = MinMaxScaler()
scaled = scaler.fit_transform(values)
train_size = int(len(scaled) * 0.67)
train, test = scaled[0:train_size], scaled[train_size:]
def create_dataset(dataset, look_back=1):
X, Y = [], []
for i in range(len(dataset) - look_back - 1):
a = dataset[i:(i + look_back), 0]
X.append(a)
Y.append(dataset[i + look_back, 0])
return np.array(X), np.array(Y)
look_back = 3
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
model = Sequential()
model.add(LSTM(50, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=100, batch_size=1)
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
trainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:, 0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = np.sqrt(mean_squared_error(testY[0], testPredict[:, 0]))
print('Test Score: %.2f RMSE' % (testScore))
plt.plot(testY[0], label='Actual')
plt.plot(testPredict[:, 0], label='Predicted')
plt.legend()
plt.show()
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