Question

这是我的代码

def create_dataset(data, look_back=1):
    dataX, dataY = [], []
    for i in range(len(data) - look_back):
        dataX.append(data[i:(i + look_back), 0])
        dataY.append(data[i + look_back, 0])
    return np.array(dataX), np.array(dataY)


look_back = 10


df = pd.read_csv('kospi.csv')
data = df.Close.values.astype('float32')
data = data.reshape(len(df), 1)


scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)


train_size = int(len(data) * 0.80)
test_size = len(data) - train_size

train = data[0:train_size]
test = data[train_size:len(data)]
x_train, y_train = create_dataset(train, look_back)
x_test, y_test = create_dataset(test, look_back)

x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))


model = Sequential()
model.add(LSTM(512, input_shape=(None, 1), return_sequences=True))
model.add(Dropout(0.3))

model.add(LSTM(512, input_shape=(None, 1)))
model.add(Dropout(0.3))

model.add(Dense(1))

model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['accuracy'])
model.summary()
hist = model.fit(x_train, y_train, epochs=10, batch_size=16, verbose=2)
p = model.predict(x_test)

plt.plot(y_test)
plt.plot(p)
plt.legend(['testY', 'p'], loc='upper right')
plt.title(mean_squared_error(y_test, p))
# plt.savefig(datetime.datetime.now())
# plt.savefig(name)
plt.show()

此模型predcit(x_test)很好用

现在我要预测下100个值

我尝试了p = model.predict(x_test[-1])

但是看起来不正确的方法

如何获得下一个N个预测值？

我认为我们可以使用model.predict解决它。

但是我不知道输入什么以及如何做出预测。

我尝试了很多，但无法解决。我该如何解决这个问题？

kospi.csv链接为https://docs.google.com/spreadsheets/d/1Q6Lh07Yg-OGtuz_Rvy979gcA65jrEqVNJNjndklCbg4/edit?usp=sharing

Answer 1

您可以使用lstm预测值。替换为以下代码：

from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential


def build_improved_model(input_dim, output_dim, return_sequences):
    """
    Builds an improved Long Short term memory model using keras.layers.recurrent.lstm
    :param input_dim: input dimension of model
    :param output_dim: ouput dimension of model
    :param return_sequences: return sequence for the model
    :return: a 3 layered LSTM model
    """
    model = Sequential()
    model.add(LSTM(
        input_shape=(None, input_dim),
        units=output_dim,
        return_sequences=return_sequences))

    model.add(Dropout(0.2))

    model.add(LSTM(
        128,
        return_sequences=False))

    model.add(Dropout(0.2))

    model.add(Dense(
        units=1))
    model.add(Activation('linear'))

    return model


def build_basic_model(input_dim, output_dim, return_sequences):
    """
    Builds a basic lstm model 
    :param input_dim: input dimension of the model
    :param output_dim: output dimension of the model
    :param return_sequences: return sequence of the model
    :return: a basic lstm model with 3 layers.
    """
    model = Sequential()
    model.add(LSTM(
        input_shape=(None, input_dim),
        units=output_dim,
        return_sequences=return_sequences))

    model.add(LSTM(
        100,
        return_sequences=False))

    model.add(Dense(
        units=1))
    model.add(Activation('linear'))

    return model

我如何使用lstm预测下一个值？

1 个答案: