In [1]:
%matplotlib inline
import math
import warnings
import numpy as np
# specify to ignore warning messages
warnings.filterwarnings("ignore")
from lstm_utils import get_raw_data
from lstm_utils import get_seq_model
from lstm_utils import get_seq_train_test
from keras.preprocessing.sequence import pad_sequences
from sklearn.metrics import mean_squared_error
In [2]:
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
sns.set_context('talk')
params = {'legend.fontsize': 'x-large',
'figure.figsize': (15, 5),
'axes.labelsize': 'x-large',
'axes.titlesize':'x-large',
'xtick.labelsize':'x-large',
'ytick.labelsize':'x-large'}
plt.rcParams.update(params)
Set Parameters¶
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TRAIN_PERCENT = 0.7
STOCK_INDEX = '^GSPC'
VERBOSE=True
Getting Data¶
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sp_df = get_raw_data(STOCK_INDEX)
sp_close_series = sp_df.Close
sp_close_series.plot()
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Preprocessing Data¶
In [5]:
# split train and test datasets
train,test,scaler = get_seq_train_test(sp_close_series,
scaling=True,
train_size=TRAIN_PERCENT)
train = np.reshape(train,(1,train.shape[0],1))
test = np.reshape(test,(1,test.shape[0],1))
train_x = train[:,:-1,:]
train_y = train[:,1:,:]
test_x = test[:,:-1,:]
test_y = test[:,1:,:]
print("Data Split Complete")
print("train_x shape={}".format(train_x.shape))
print("train_y shape={}".format(train_y.shape))
print("test_x shape={}".format(test_x.shape))
print("test_y shape={}".format(test_y.shape))
Prepare LSTM Model¶
In [6]:
# build RNN model
seq_lstm_model=None
try:
seq_lstm_model = get_seq_model(input_shape=(train_x.shape[1],1),
verbose=VERBOSE)
except:
print("Model Build Failed. Trying Again")
seq_lstm_model = get_seq_model(input_shape=(train_x.shape[1],1),
verbose=VERBOSE)
Fit the Model¶
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# train the model
seq_lstm_model.fit(train_x, train_y,
epochs=150, batch_size=1,
verbose=2)
print("Model Fit Complete")
Train Prediction Performance¶
In [8]:
# train fit performance
trainPredict = seq_lstm_model.predict(train_x)
trainScore = math.sqrt(mean_squared_error(train_y[0], trainPredict[0]))
print('Train Score: %.2f RMSE' % (trainScore))
Test Prediction Performance¶
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# Pad input sequence
testPredict = pad_sequences(test_x,
maxlen=train_x.shape[1],
padding='post',
dtype='float64')
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# forecast values
testPredict = seq_lstm_model.predict(testPredict)
# evaluate performances
testScore = math.sqrt(mean_squared_error(test_y[0],
testPredict[0][:test_x.shape[1]]))
print('Test Score: %.2f RMSE' % (testScore))
Plot Test Predictions¶
In [11]:
# inverse transformation
trainPredict = scaler.inverse_transform(trainPredict.\
reshape(trainPredict.shape[1]))
testPredict = scaler.inverse_transform(testPredict.\
reshape(testPredict.shape[1]))
In [12]:
# plot the true and forecasted values
train_size = len(trainPredict)+1
plt.plot(sp_close_series.index,
sp_close_series.values,c='black',
alpha=0.3,label='True Data')
plt.plot(sp_close_series.index[1:train_size],
trainPredict,label='Training Fit',c='g')
plt.plot(sp_close_series.index[train_size+1:],
testPredict[:test_x.shape[1]],label='Testing Forecast')
plt.title('Forecast Plot')
plt.legend()
plt.show()
