Question

我有一个包含3列的数据集。

|Date    |Price      |Sentiment   |
|---------------------------------|
|Date 1  |Price 1    |S1          |
|------------------------- -------| 
|Date 2  |Price 2    |S2          |
|------------------------- -------| 
|Date 3  |Price 3    |S3          |
|------------------------- -------|

在此数据集中，＆＃39; Price＆＃39;取决于＆＃39;情绪＆＃39;值，即如果情绪值为正，则价格将增加，否则将减少。我需要预测＆＃39; Price＆＃39;使用Keras。现在我使用this link中的代码来预测价格＆＃39;但它没有考虑“情绪”和“情感”。预测值。

import numpy
import matplotlib.pyplot as plt
from pandas import read_csv
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler

# 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 numpy.array(dataX), numpy.array(dataY)

# fix random seed for reproducibility
numpy.random.seed(7)

# load the dataset
dataframe = read_csv('./dataset/combined.csv', usecols=[1], engine='python')
dataset = dataframe.values
print (dataset)
dataset = dataset.astype('float64')

# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
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 = 3
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# reshape input to be [samples, time steps, features]
trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], 1))

# create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_shape=(look_back, 1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=1, batch_size=1, verbose=2)

# make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

# invert predictions
print (trainPredict)
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 = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
#print('Train Score: %.2f RMSE' % (trainScore))
#testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
#print('Test Score: %.2f RMSE' % (testScore))

# shift train predictions for plotting
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict

# shift test predictions for plotting
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict

# plot baseline and predictions
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()

我如何预测＆＃39; Price＆＃39;通过考虑两个＆＃39; Price＆＃39;和＆＃39;情绪＆＃39;列？

Answer 1

首先要做的是实际加载相关列，所以更改

dataframe = read_csv('./dataset/combined.csv', usecols=[1], engine='python')

到

dataframe = read_csv('./dataset/combined.csv', usecols=[1, 2], engine='python')

我没有看到明确引用的列，所以我认为这应该足够了，但我可能会遗漏一些明显的内容。

Python | Keras |多变量预测

1 个答案: