View source for Keras-timeseries-stock-tata-predict

Sumber: https://www.kdnuggets.com/2018/11/keras-long-short-term-memory-lstm-model-predict-stock-prices.html



 # '''
 # https://www.kdnuggets.com/2018/11/keras-long-short-term-memory-lstm-model-predict-stock-prices.html
 # '''
 
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 
 # https://raw.githubusercontent.com/mwitiderrick/stockprice/master/NSE-TATAGLOBAL.csv
 dataset_train = pd.read_csv('NSE-TATAGLOBAL.csv')
 training_set = dataset_train.iloc[:, 1:2].values
 
 # check head
 dataset_train.head()
 
 # scaling
 from sklearn.preprocessing import MinMaxScaler
 sc = MinMaxScaler(feature_range = (0, 1))
 training_set_scaled = sc.fit_transform(training_set)
 
 # create data with time step
 X_train = []
 y_train = []
 for i in range(60, 2035):
     X_train.append(training_set_scaled[i-60:i, 0])
     y_train.append(training_set_scaled[i, 0])
 X_train, y_train = np.array(X_train), np.array(y_train) 
 
 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
 
 # train
 from keras.models import Sequential
 from keras.layers import Dense
 from keras.layers import LSTM
 from keras.layers import Dropout
 
 regressor = Sequential()
 regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
 regressor.add(Dropout(0.2))
 regressor.add(LSTM(units = 50, return_sequences = True))
 regressor.add(Dropout(0.2))
 regressor.add(LSTM(units = 50, return_sequences = True))
 regressor.add(Dropout(0.2))
 regressor.add(LSTM(units = 50))
 regressor.add(Dropout(0.2))
 regressor.add(Dense(units = 1))
 regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')
 regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)
 
 # test
 # https://raw.githubusercontent.com/mwitiderrick/stockprice/master/tatatest.csv
 dataset_test = pd.read_csv('tatatest.csv')
 real_stock_price = dataset_test.iloc[:, 1:2].values
 
 dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)
 inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
 inputs = inputs.reshape(-1,1)
 inputs = sc.transform(inputs)
 X_test = []
 for i in range(60, 76):
     X_test.append(inputs[i-60:i, 0])
 X_test = np.array(X_test)
 X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
 predicted_stock_price = regressor.predict(X_test)
 predicted_stock_price = sc.inverse_transform(predicted_stock_price)
 
 # Plot
 plt.plot(real_stock_price, color = 'black', label = 'TATA Stock Price')
 plt.plot(predicted_stock_price, color = 'green', label = 'Predicted TATA Stock Price')
 plt.title('TATA Stock Price Prediction')
 plt.xlabel('Time')
 plt.ylabel('TATA Stock Price')
 plt.legend()
 plt.show()

==Pranala Menarik==

* [[Keras]]