Difference between revisions of "Keras-timeseries-stock-tata-predict"
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Onnowpurbo (talk | contribs) (Created page with "Sumber: https://www.kdnuggets.com/2018/11/keras-long-short-term-memory-lstm-model-predict-stock-prices.html") |
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Sumber: https://www.kdnuggets.com/2018/11/keras-long-short-term-memory-lstm-model-predict-stock-prices.html | 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]] |
Latest revision as of 08:11, 6 August 2019
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()