(PyTorch)TCN和RNN/LSTM/GRU结合实现时间序列预测

本文介绍: (PyTorch)TCN和RNN/LSTM/GRU结合实现时间序列预测

I. 前言

前面已经写了一系列有关LSTM时间序列预测的文章：

时间卷积网络TCN和CNN都是一种利用卷积操作提取特征的模型，CNN是通过卷积层来提取图像中的特征，而TCN则通过时序卷积层来处理时间序列数据。TCN强调如何使用非常深的网络（residual）和膨胀卷积的组合来扩大感受野进而捕捉更广泛的上下文信息。

有关TCN的原理部分不做过多讲解，原理比较简单，下面直接讲解代码。

II. TCN

class Chomp1d(nn.Module):
    def __init__(self, chomp_size):
        super(Chomp1d, self).__init__()
        self.chomp_size = chomp_size

    def forward(self, x):
        """
        裁剪的模块，裁剪多出来的padding
        """
        return x[:, :, :-self.chomp_size].contiguous()


class TemporalBlock(nn.Module):
    def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding, dropout=0.2):
        """
        相当于一个Residual block

        :param n_inputs: int, 输入通道数
        :param n_outputs: int, 输出通道数
        :param kernel_size: int, 卷积核尺寸
        :param stride: int, 步长，一般为1
        :param dilation: int, 膨胀系数
        :param padding: int, 填充系数
        :param dropout: float, dropout比率
        """
        super(TemporalBlock, self).__init__()
        self.conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size,
                                           stride=stride, padding=padding, dilation=dilation))
        # 经过conv1，输出的size其实是(Batch, input_channel, seq_len + padding)
        self.chomp1 = Chomp1d(padding)  # 裁剪掉多出来的padding部分，维持输出时间步为seq_len
        self.relu1 = nn.ReLU()
        self.dropout1 = nn.Dropout(dropout)

        self.conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size,
                                           stride=stride, padding=padding, dilation=dilation))
        self.chomp2 = Chomp1d(padding)  # 裁剪掉多出来的padding部分，维持输出时间步为seq_len
        self.relu2 = nn.ReLU()
        self.dropout2 = nn.Dropout(dropout)

        self.net = nn.Sequential(self.conv1, self.chomp1, self.relu1, self.dropout1,
                                 self.conv2, self.chomp2, self.relu2, self.dropout2)
        self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None
        self.relu = nn.ReLU()
        self.init_weights()

    def init_weights(self):
        """
        参数初始化

        :return:
        """
        self.conv1.weight.data.normal_(0, 0.01)
        self.conv2.weight.data.normal_(0, 0.01)
        if self.downsample is not None:
            self.downsample.weight.data.normal_(0, 0.01)

    def forward(self, x):
        """
        :param x: size of (Batch, input_channel, seq_len)
        :return:
        """
        out = self.net(x)
        res = x if self.downsample is None else self.downsample(x)
        return self.relu(out + res)


class TCN(nn.Module):
    def __init__(self, num_inputs, channels, kernel_size=2, dropout=0.2):
        """
        :param num_inputs: int， 输入通道数
        :param channels: list，每层的hidden_channel数，例如[25,25,25,25]表示有4个隐层，每层hidden_channel数为25
        :param kernel_size: int, 卷积核尺寸
        :param dropout: float, drop_out比率
        """
        super(TCN, self).__init__()
        super().__init__()
        layers = []
        num_levels = len(channels)
        for i in range(num_levels):
            dilation_size = 2 ** i  # 膨胀系数：1，2，4，8……
            in_channels = num_inputs if i == 0 else channels[i - 1]  # 确定每一层的输入通道数
            out_channels = channels[i]  # 确定每一层的输出通道数
            layers += [TemporalBlock(in_channels, out_channels, kernel_size, stride=1, dilation=dilation_size,
                                     padding=(kernel_size - 1) * dilation_size, dropout=dropout)]

        self.network = nn.Sequential(*layers)

    def forward(self, x):
        """
        :param x: size of (Batch, input_channel, seq_len)
        :return: size of (Batch, output_channel, seq_len)
        """
        x = self.network(x)
        return x

可以看到这里TCN输入的尺寸是(batch_size, input_channel, seq_len)，输出尺寸是(batch_size, output_channel, seq_len)。这与前面讲的文章大致类似，如果需要直接利用TCN得到输出，可以取输出的最后一个时间步，然后经过一个nn.Linear即可得到预测结果，即：

self.fc = nn.Linear(channels[-1], output_size)
x = x[:, :, -1]
x = self.fc(x)

III. TCN-RNN/LSTM/GRU

TCN的输出尺寸为(batch_size, output_channel, seq_len)，这天然满足了RNN类模型的输入要求，因此将时序数据先经过TCN再经过RNN等模型是很自然的想法。

3.1 TCN-RNN

TCN-RNN模型搭建如下：

class TCN_RNN(nn.Module):
    def __init__(self):
        super(TCN_RNN, self).__init__()
        self.tcn = TCN(num_inputs=7, channels=[32, 32, 32])
        self.rnn = nn.RNN(input_size=32, hidden_size=64,
                          num_layers=2, batch_first=True)
        self.fc = nn.Linear(64, 1)

    def forward(self, x):
        x = x.permute(0, 2, 1)  # b i s
        x = self.tcn(x)  # b h s
        x = x.permute(0, 2, 1)  # b s h
        x, _ = self.rnn(x)  # b, s, h
        x = x[:, -1, :]
        x = self.fc(x)  # b output_size
        return x

由于我们构建的输入为(batch_size, seq_len, input_size)，而TCN要求的输入为(batch_size, input_channel, seq_len)，因此首先需要进行一个permute操作。经过TCN后，输出为(batch_size, output_channel, seq_len)，其中output_channel为channels=[32, 32, 32]中最后一个数，即32。

接着RNN的输入应该为(batch_size, seq_len, output_channel)，因此还需要经过一个permute。最后利用一个nn.Linear得到这个batch的预测结果。

3.2 TCN-LSTM

相比TCN-RNN，TCN-LSTM只是进行了简单替换：

class TCN_LSTM(nn.Module):
    def __init__(self):
        super(TCN_LSTM, self).__init__()
        self.tcn = TCN(num_inputs=7, channels=[32, 32, 32])
        self.lstm = nn.LSTM(input_size=32, hidden_size=64,
                            num_layers=2, batch_first=True)
        self.fc = nn.Linear(64, 1)

    def forward(self, x):
        x = x.permute(0, 2, 1)  # b i s
        x = self.tcn(x)  # b h s
        x = x.permute(0, 2, 1)  # b s h
        x, _ = self.lstm(x)  # b, s, h
        x = x[:, -1, :]
        x = self.fc(x)  # b output_size
        return x

3.3 TCN-GRU

TCN-GRU类似：

class TCN_GRU(nn.Module):
    def __init__(self):
        super(TCN_GRU, self).__init__()
        self.tcn = TCN(num_inputs=7, channels=[32, 32, 32])
        self.gru = nn.GRU(input_size=32, hidden_size=64,
                          num_layers=2, batch_first=True)
        self.fc = nn.Linear(64, 1)

    def forward(self, x):
        x = x.permute(0, 2, 1)  # b i s
        x = self.tcn(x)  # b h s
        x = x.permute(0, 2, 1)  # b s h
        x, _ = self.gru(x)  # b, s, h
        x = x[:, -1, :]
        x = self.fc(x)  # b output_size
        return x