time-series - 使用 predict_generator 时预测长度和测试数据长度的变化

Question

df = pd.read_csv('F:/series.csv')

train, validate, test = df[0:60], df[60:80], df[80:100]

sc = MinMaxScaler(feature_range = (-1, 1))

train = sc.fit_transform(train)

validate = sc.fit_transform(validate)

test = sc.fit_transform(test)


train = train.reshape((len(train),1))

test = test.reshape((len(test),1))

validate = validate.reshape((len(validate),1))

n_input = 5

n_features = 1

generator_train = TimeseriesGenerator(train, train, length=n_input, batch_size=2)

generator_validate = TimeseriesGenerator(validate, validate, length=n_input, batch_size=2)

generator_test = TimeseriesGenerator(test, test, length=n_input, batch_size=2)

model = Sequential()

model.add(LSTM(200, return_sequences = True,  input_shape=(n_input, n_features)))

model.add(Dropout(0.2))

model.add(LSTM(200))

model.add(Dense(units = 1))

model.compile(loss='mean_squared_error', optimizer='adam')

history = model.fit_generator(generator_train, epochs= 100, validation_data = generator_validate)

model.evaluate_generator(generator_test)

prediction = model.predict_generator(generator_test, steps = 5)

prediction.shape
(10,1)

test.shape
(20,1)

这让我很困惑，如何解决这个问题？如何评估预测和测试数据？我犯了什么错误？

标签： time-seriesgenerator

Answer 1

我刚刚找到了答案，预测生成器长度取决于 (test_generator*batch_size) 的长度。现在测量 RMSE 消除测试数据的第一个 n_input 长度。现在大小变得相等。