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October 11, 2023 02:21
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# LSTM for international airline passengers problem with regression framing | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from pandas import read_csv | |
import math | |
import tensorflow as tf | |
from tensorflow.keras.models import Sequential | |
from tensorflow.keras.layers import Dense | |
from tensorflow.keras.layers import LSTM | |
from sklearn.preprocessing import MinMaxScaler | |
from sklearn.metrics import mean_squared_error | |
# convert an array of values into a dataset matrix | |
def create_dataset(dataset, look_back=1): | |
dataX, dataY = [], [] | |
for i in range(len(dataset)-look_back-1): | |
a = dataset[i:(i+look_back), 0] | |
dataX.append(a) | |
dataY.append(dataset[i + look_back, 0]) | |
return np.array(dataX), np.array(dataY) | |
# fix random seed for reproducibility | |
tf.random.set_seed(7) | |
# load the dataset | |
#dataframe = read_csv('airline-passengers.csv', usecols=[1], engine='python') | |
dataframe = read_csv('data/sp500.csv', usecols=[4], engine='python') | |
dataset = dataframe.values | |
dataset = dataset.astype('float32') | |
# normalize the dataset | |
scaler = MinMaxScaler(feature_range=(0, 1)) | |
#train | |
dataset = scaler.fit_transform(dataset) | |
# split into train and test sets | |
train_size = int(len(dataset) * 0.67) | |
test_size = len(dataset) - train_size | |
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:] | |
# reshape into X=t and Y=t+1 | |
look_back = 1 | |
trainX, trainY = create_dataset(train, look_back) | |
testX, testY = create_dataset(test, look_back) | |
# reshape input to be [samples, time steps, features] | |
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) | |
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) | |
# create and fit the LSTM network | |
model = Sequential() | |
model.add(LSTM(4, 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, verbose=2) | |
# make predictions | |
trainPredict = model.predict(trainX) | |
testPredict = model.predict(testX) | |
# invert predictions | |
trainPredict = scaler.inverse_transform(trainPredict) | |
trainY = scaler.inverse_transform([trainY]) | |
testPredict = scaler.inverse_transform(testPredict) | |
testY = scaler.inverse_transform([testY]) | |
# calculate root mean squared error | |
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)) | |
# shift train predictions for plotting | |
trainPredictPlot = np.empty_like(dataset) | |
trainPredictPlot[:, :] = np.nan | |
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict | |
# shift test predictions for plotting | |
testPredictPlot = np.empty_like(dataset) | |
testPredictPlot[:, :] = np.nan | |
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict | |
# plot baseline and predictions | |
plt.plot(scaler.inverse_transform(dataset), label='Inverse dataset') | |
#plt.plot(trainPredictPlot, label='trainPredictPlot') | |
plt.plot(testPredictPlot, label='testPredictPlot') | |
plt.legend() | |
plt.show() |
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