Question

我使用每日需求数据在keras上使用 LSTM神经网络建立此模型

    import numpy as np
    import pandas as pd 
    from sklearn import preprocessing
    from keras.layers.core import Dense, Dropout, Activation
    from keras.activations import linear
    from keras.layers.recurrent import LSTM
    from keras.models import Sequential
    from matplotlib import pyplot

    #read and prepare data from datafile
    data_file_name = "DailyDemand.csv"
    data_csv = pd.read_csv(data_file_name, delimiter = ';',header=None, usecols=[1,2,3,4,5])
    yt = data_csv[1:]
    data = yt
    data.columns = ['MoyenneTransactHier', 'MaxTransaction', 'MinTransaction','CountTransaction','Demand']
    # print (data.head(10))
    pd.options.display.float_format = '{:,.0f}'.format
    data = data.dropna ()
    y=data['Demand'].astype(int)
    cols=['MoyenneTransactHier', 'MaxTransaction', 'MinTransaction','CountTransaction']
    x=data[cols].astype(int)

    #scaling data
    scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
    x = np.array(x).reshape ((len(x),4 ))
    x = scaler_x.fit_transform(x)
    scaler_y = preprocessing.MinMaxScaler(feature_range =(-1, 1))
    y = np.array(y).reshape ((len(y), 1))
    y = scaler_y.fit_transform(y)
    print("longeur de y",len(y))
    # Split train and test data
    train_end = 400
    x_train=x[0: train_end ,]
    x_test=x[train_end +1: ,]
    y_train=y[0: train_end]
    y_test=y[train_end +1:] 
    x_train=x_train.reshape(x_train.shape +(1,))
    x_test=x_test.reshape(x_test.shape + (1,))

    print("Data well prepared")
    print ('x_train shape ', x_train.shape)
    print ('y_train', y_train.shape)

    #Design the model - LSTM Network
    seed = 2016
    np.random.seed(seed)
    fit1 = Sequential ()
    fit1.add(LSTM(
        output_dim = 4,
        activation='tanh',
        input_shape =(4, 1)))
    fit1.add(Dense(output_dim =1))
    fit1.add(Activation(linear))
    #rmsprop or sgd
    batchsize = 1
    fit1.compile(loss="mean_squared_error",optimizer="rmsprop")
    #train the model
    fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =20, shuffle=True)

    print(fit1.summary ())

    #Model error
    score_train = fit1.evaluate(x_train ,y_train ,batch_size =batchsize)
    score_test = fit1.evaluate(x_test , y_test ,batch_size =batchsize)
    print("in  train  MSE = ",round(score_train,4))
    print("in test  MSE = ",round(score_test ,4))

    #Make prediction
    pred1=fit1.predict(x_test)
    pred1 = scaler_y.inverse_transform(np.array(pred1).reshape ((len(pred1), 1)))
    real_test = scaler_y.inverse_transform(np.array(y_test).reshape ((len(y_test), 1))).astype(int)

    #save prediction
    testData = pd.DataFrame(real_test)
    preddData = pd.DataFrame(pred1)
    dataF = pd.concat([testData,preddData], axis=1)
    dataF.columns =['Real demand','Predicted Demand']
    dataF.to_csv('Demandprediction.csv')

    pyplot.plot(pred1, label='Forecast')
    pyplot.plot(real_test,label='Actual')
    pyplot.legend()
    pyplot.show()

它会产生这样的结果：

在我拥有的历史数据上建立一个好的模型后，我不知道如何能够预测未来的价值观？例如，未来10天的需求（数据是每天）？

Answer 1

例如，为了预测10天的数据，最后一层需要10个输出

fit1.add(Dense(10))

您需要相应地训练模型，并使用历史数据中的首选输入和输出。例如，如果您希望模型能够获取10天的数据并预测未来10天的数据，那么您将需要通过历史数据中的相应输入 - 输出对10来训练模型

如何使用keras预测时间范围的未来值？

1 个答案: