Question

我正在构建深度学习lstm时间序列预测模型。我已阅读这篇文章：https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/

我正在使用单变量数据进行多步输出预测。

对于香草lstm和编码器模型，我尝试过以下代码：

raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150]

# split a univariate sequence into samples
def split_sequence(sequence, n_steps_in, n_steps_out):
    X, y = list(), list()
    for i in range(len(sequence)):
        # find the end of this pattern
        end_ix = i + n_steps_in
        out_end_ix = end_ix + n_steps_out
        # check if we are beyond the sequence
        if out_end_ix > len(sequence):
            break
        # gather input and output parts of the pattern
        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix:out_end_ix]
        X.append(seq_x)
        y.append(seq_y)
    return array(X), array(y)

# choose a number of time steps
n_steps_in, n_steps_out = 5, 5

# split into samples
X, y = split_sequence(raw_seq, n_steps_in, n_steps_out)
print(X.shape)
print(y.shape)

# reshape from [samples, timesteps] into [samples, timesteps, features]
n_features = 1
X = X.reshape((X.shape[0], X.shape[1], n_features))

例如，对于ConvLSTM模型，我需要根据文章转换我的系列形状：

# reshape from [samples, timesteps] into [samples, timesteps, rows, columns, features]

模型需要这样的输入：

model.add(ConvLSTM2D(filters=64, kernel_size=(1,2), activation='relu', input_shape=(n_seq, 1, n_steps, n_features)))

我可以将我的单变量序列转换为香草lstm，将编码器-解码器模型转换为多输出模型：

# reshape from [samples, timesteps] into [samples, timesteps, features]

但是我无法将我的系列转换为CNN-LSTM和ConvLSTM的多输出模型：

# reshape from [samples, timesteps] into [samples, timesteps, rows, columns, features]

如何为多输出模型重塑CNN-LSTM和ConvLSTM系列？

如何为keras中的CNN-LSTM和ConvLSTM的多步输出模型重塑数据？

0 个答案: