Difference between revisions of "Keras-timeseries-stock-tata-predict"

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

Pranala Menarik