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PyTorch搭建LSTM實(shí)現(xiàn)多變量多步長(zhǎng)時(shí)序負(fù)荷預(yù)測(cè)

更新時(shí)間：2022年05月11日 09:55:06 作者：Cyril_KI

這篇文章主要為大家介紹了PyTorch搭建LSTM實(shí)現(xiàn)多變量多步長(zhǎng)時(shí)序負(fù)荷預(yù)測(cè)，有需要的朋友可以借鑒參考下，希望能夠有所幫助，祝大家多多進(jìn)步，早日升職加薪

I. 前言

在前面的兩篇文章PyTorch搭建LSTM實(shí)現(xiàn)時(shí)間序列預(yù)測(cè)（負(fù)荷預(yù)測(cè)）和PyTorch搭建LSTM實(shí)現(xiàn)多變量時(shí)間序列預(yù)測(cè)（負(fù)荷預(yù)測(cè)）中，我們利用LSTM分別實(shí)現(xiàn)了單變量單步長(zhǎng)時(shí)間序列預(yù)測(cè)和多變量單步長(zhǎng)時(shí)間序列預(yù)測(cè)。

本篇文章主要考慮用PyTorch搭建LSTM實(shí)現(xiàn)多變量多步長(zhǎng)時(shí)間序列預(yù)測(cè)。

系列文章：

PyTorch搭建雙向LSTM實(shí)現(xiàn)時(shí)間序列負(fù)荷預(yù)測(cè)

PyTorch搭建LSTM實(shí)現(xiàn)多變量時(shí)序負(fù)荷預(yù)測(cè)

PyTorch深度學(xué)習(xí)LSTM從input輸入到Linear輸出

PyTorch搭建LSTM實(shí)現(xiàn)時(shí)間序列負(fù)荷預(yù)測(cè)

II. 數(shù)據(jù)處理

數(shù)據(jù)集為某個(gè)地區(qū)某段時(shí)間內(nèi)的電力負(fù)荷數(shù)據(jù)，除了負(fù)荷以外，還包括溫度、濕度等信息。

本文中，我們根據(jù)前24個(gè)時(shí)刻的負(fù)荷以及該時(shí)刻的環(huán)境變量來預(yù)測(cè)接下來4個(gè)時(shí)刻的負(fù)荷（步長(zhǎng)可調(diào)）。

def load_data(file_name):
    global MAX, MIN
    df = pd.read_csv(os.path.dirname(os.getcwd()) + '/data/new_data/' + file_name, encoding='gbk')
    columns = df.columns
    df.fillna(df.mean(), inplace=True)
    MAX = np.max(df[columns[1]])
    MIN = np.min(df[columns[1]])
    df[columns[1]] = (df[columns[1]] - MIN) / (MAX - MIN)
    return df
class MyDataset(Dataset):
    def __init__(self, data):
        self.data = data
    def __getitem__(self, item):
        return self.data[item]
    def __len__(self):
        return len(self.data)
def nn_seq(file_name, B, num):
    print('data processing...')
    data = load_data(file_name)
    load = data[data.columns[1]]
    load = load.tolist()
    data = data.values.tolist()
    seq = []
    for i in range(0, len(data) - 24 - num, num):
        train_seq = []
        train_label = []
        for j in range(i, i + 24):
            x = [load[j]]
            for c in range(2, 8):
                x.append(data[j][c])
            train_seq.append(x)
        for j in range(i + 24, i + 24 + num):
            train_label.append(load[j])
        train_seq = torch.FloatTensor(train_seq)
        train_label = torch.FloatTensor(train_label).view(-1)
        seq.append((train_seq, train_label))
    # print(seq[-1])
    Dtr = seq[0:int(len(seq) * 0.7)]
    Dte = seq[int(len(seq) * 0.7):len(seq)]
    train_len = int(len(Dtr) / B) * B
    test_len = int(len(Dte) / B) * B
    Dtr, Dte = Dtr[:train_len], Dte[:test_len]
    train = MyDataset(Dtr)
    test = MyDataset(Dte)
    Dtr = DataLoader(dataset=train, batch_size=B, shuffle=False, num_workers=0)
    Dte = DataLoader(dataset=test, batch_size=B, shuffle=False, num_workers=0)
    return Dtr, Dte

其中num表示需要預(yù)測(cè)的步長(zhǎng)，如num=4表示預(yù)測(cè)接下來4個(gè)時(shí)刻的負(fù)荷。

任意輸出其中一條數(shù)據(jù)：

(tensor([[0.5830, 1.0000, 0.9091, 0.6957, 0.8333, 0.4884, 0.5122],
        [0.6215, 1.0000, 0.9091, 0.7391, 0.8333, 0.4884, 0.5122],
        [0.5954, 1.0000, 0.9091, 0.7826, 0.8333, 0.4884, 0.5122],
        [0.5391, 1.0000, 0.9091, 0.8261, 0.8333, 0.4884, 0.5122],
        [0.5351, 1.0000, 0.9091, 0.8696, 0.8333, 0.4884, 0.5122],
        [0.5169, 1.0000, 0.9091, 0.9130, 0.8333, 0.4884, 0.5122],
        [0.4694, 1.0000, 0.9091, 0.9565, 0.8333, 0.4884, 0.5122],
        [0.4489, 1.0000, 0.9091, 1.0000, 0.8333, 0.4884, 0.5122],
        [0.4885, 1.0000, 0.9091, 0.0000, 1.0000, 0.3256, 0.3902],
        [0.4612, 1.0000, 0.9091, 0.0435, 1.0000, 0.3256, 0.3902],
        [0.4229, 1.0000, 0.9091, 0.0870, 1.0000, 0.3256, 0.3902],
        [0.4173, 1.0000, 0.9091, 0.1304, 1.0000, 0.3256, 0.3902],
        [0.4503, 1.0000, 0.9091, 0.1739, 1.0000, 0.3256, 0.3902],
        [0.4502, 1.0000, 0.9091, 0.2174, 1.0000, 0.3256, 0.3902],
        [0.5426, 1.0000, 0.9091, 0.2609, 1.0000, 0.3256, 0.3902],
        [0.5579, 1.0000, 0.9091, 0.3043, 1.0000, 0.3256, 0.3902],
        [0.6035, 1.0000, 0.9091, 0.3478, 1.0000, 0.3256, 0.3902],
        [0.6540, 1.0000, 0.9091, 0.3913, 1.0000, 0.3256, 0.3902],
        [0.6181, 1.0000, 0.9091, 0.4348, 1.0000, 0.3256, 0.3902],
        [0.6334, 1.0000, 0.9091, 0.4783, 1.0000, 0.3256, 0.3902],
        [0.6297, 1.0000, 0.9091, 0.5217, 1.0000, 0.3256, 0.3902],
        [0.5610, 1.0000, 0.9091, 0.5652, 1.0000, 0.3256, 0.3902],
        [0.5957, 1.0000, 0.9091, 0.6087, 1.0000, 0.3256, 0.3902],
        [0.6427, 1.0000, 0.9091, 0.6522, 1.0000, 0.3256, 0.3902]]), tensor([0.6360, 0.6996, 0.6889, 0.6434]))

數(shù)據(jù)格式為(X, Y)。其中X一共24行，表示前24個(gè)時(shí)刻的負(fù)荷值和該時(shí)刻的環(huán)境變量。Y一共四個(gè)值，表示需要預(yù)測(cè)的四個(gè)負(fù)荷值。需要注意的是，此時(shí)input_size=7，output_size=4。

III. LSTM模型

這里采用了深入理解PyTorch中LSTM的輸入和輸出（從input輸入到Linear輸出）中的模型：

class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size):
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.output_size = output_size
        self.num_directions = 1
        self.batch_size = batch_size
        self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True)
        self.linear = nn.Linear(self.hidden_size, self.output_size)
    def forward(self, input_seq):
        h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(device)
        c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(device)
        # print(input_seq.size())
        seq_len = input_seq.shape[1]
        # input(batch_size, seq_len, input_size)
        input_seq = input_seq.view(self.batch_size, seq_len, self.input_size)
        # output(batch_size, seq_len, num_directions * hidden_size)
        output, _ = self.lstm(input_seq, (h_0, c_0))
        # print('output.size=', output.size())
        # print(self.batch_size * seq_len, self.hidden_size)
        output = output.contiguous().view(self.batch_size * seq_len, self.hidden_size)  # (5 * 30, 64)
        pred = self.linear(output)  # pred()
        # print('pred=', pred.shape)
        pred = pred.view(self.batch_size, seq_len, -1)
        pred = pred[:, -1, :]
        return pred

IV. 訓(xùn)練和預(yù)測(cè)

訓(xùn)練和預(yù)測(cè)代碼和前幾篇都差不多，只是需要注意input_size和output_size的大小。

訓(xùn)練了100輪，預(yù)測(cè)接下來四個(gè)時(shí)刻的負(fù)荷值，MAPE為7.53%：

V. 源碼及數(shù)據(jù)

源碼及數(shù)據(jù)我放在了GitHub上，LSTM-Load-Forecasting

以上就是PyTorch搭建LSTM實(shí)現(xiàn)多變量多步長(zhǎng)時(shí)序負(fù)荷預(yù)測(cè)的詳細(xì)內(nèi)容，更多關(guān)于LSTM多變量多步長(zhǎng)時(shí)序負(fù)荷預(yù)測(cè)的資料請(qǐng)關(guān)注腳本之家其它相關(guān)文章！

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