PyTorch搭建LSTM实现时间序列预测（负荷预测）_lstm pytorch

作者：知新_RL | 2024-05-14 16:39:09

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lstm pytorch

I. 前言

在上一篇文章深入理解PyTorch中LSTM的输入和输出（从input输入到Linear输出）中，我详细地解释了如何利用PyTorch来搭建一个LSTM模型，本篇文章的主要目的是搭建一个LSTM模型用于时间序列预测。

系列文章：

II. 数据处理

数据集为某个地区某段时间内的电力负荷数据，除了负荷以外，还包括温度、湿度等信息。

本篇文章暂时不考虑其它变量，只考虑用历史负荷来预测未来负荷。本文中，我们根据前24个时刻的负荷下一时刻的负荷。有关多变量预测请参考：PyTorch搭建LSTM实现多变量时间序列预测（负荷预测）。

def load_data(file_name):
    df = pd.read_csv('data/new_data/' + file_name, encoding='gbk')
    columns = df.columns
    df.fillna(df.mean(), inplace=True)
    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_us(B):
    print('data processing...')
    dataset = load_data()
    # split
    train = dataset[:int(len(dataset) * 0.6)]
    val = dataset[int(len(dataset) * 0.6):int(len(dataset) * 0.8)]
    test = dataset[int(len(dataset) * 0.8):len(dataset)]
    m, n = np.max(train[train.columns[1]]), np.min(train[train.columns[1]])

    def process(data, batch_size, shuffle):
        load = data[data.columns[1]]
        load = load.tolist()
        data = data.values.tolist()
        load = (load - n) / (m - n)
        seq = []
        for i in range(len(data) - 24):
            train_seq = []
            train_label = []
            for j in range(i, i + 24):
                x = [load[j]]
                train_seq.append(x)
            # for c in range(2, 8):
            #     train_seq.append(data[i + 24][c])
            train_label.append(load[i + 24])
            train_seq = torch.FloatTensor(train_seq)
            train_label = torch.FloatTensor(train_label).view(-1)
            seq.append((train_seq, train_label))

        # print(seq[-1])
        seq = MyDataset(seq)
        seq = DataLoader(dataset=seq, batch_size=batch_size, shuffle=shuffle, num_workers=0, drop_last=True)

        return seq

    Dtr = process(train, B, True)
    Val = process(val, B, True)
    Dte = process(test, B, False)

    return Dtr, Val, Dte, m, n
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上面代码用了DataLoader来对原始数据进行处理，最终得到了batch_size=B的数据集Dtr、Val以及Dte，Dtr为训练集，Val为验证集，Dte为测试集。

III. LSTM模型

这里采用了深入理解PyTorch中LSTM的输入和输出（从input输入到Linear输出）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 # 单向LSTM
        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):
        batch_size, seq_len = input_seq.shape[0], input_seq.shape[1]
        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)
        # output(batch_size, seq_len, num_directions * hidden_size)
        output, _ = self.lstm(input_seq, (h_0, c_0)) # output(5, 30, 64)
        pred = self.linear(output)  # (5, 30, 1)
        pred = pred[:, -1, :]  # (5, 1)
        return pred

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IV. 训练

def train(args, Dtr, Val, path):
    input_size, hidden_size, num_layers = args.input_size, args.hidden_size, args.num_layers
    output_size = args.output_size
    if args.bidirectional:
        model = BiLSTM(input_size, hidden_size, num_layers, output_size, batch_size=args.batch_size).to(device)
    else:
        model = LSTM(input_size, hidden_size, num_layers, output_size, batch_size=args.batch_size).to(device)

    loss_function = nn.MSELoss().to(device)
    if args.optimizer == 'adam':
        optimizer = torch.optim.Adam(model.parameters(), lr=args.lr,
                                     weight_decay=args.weight_decay)
    else:
        optimizer = torch.optim.SGD(model.parameters(), lr=args.lr,
                                    momentum=0.9, weight_decay=args.weight_decay)
    scheduler = StepLR(optimizer, step_size=args.step_size, gamma=args.gamma)
    # training
    min_epochs = 10
    best_model = None
    min_val_loss = 5
    for epoch in tqdm(range(args.epochs)):
        train_loss = []
        for (seq, label) in Dtr:
            seq = seq.to(device)
            label = label.to(device)
            y_pred = model(seq)
            loss = loss_function(y_pred, label)
            train_loss.append(loss.item())
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

        scheduler.step()
        # validation
        val_loss = get_val_loss(args, model, Val)
        if epoch > min_epochs and val_loss < min_val_loss:
            min_val_loss = val_loss
            best_model = copy.deepcopy(model)

        print('epoch {:03d} train_loss {:.8f} val_loss {:.8f}'.format(epoch, np.mean(train_loss), val_loss))
        model.train()

    state = {'models': best_model.state_dict()}
    torch.save(state, path)
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保存训练过程中在验证集上表现最好的模型。

V. 测试

def test(args, Dte, path, m, n):
    pred = []
    y = []
    print('loading models...')
    input_size, hidden_size, num_layers = args.input_size, args.hidden_size, args.num_layers
    output_size = args.output_size
    if args.bidirectional:
        model = BiLSTM(input_size, hidden_size, num_layers, output_size, batch_size=args.batch_size).to(device)
    else:
        model = LSTM(input_size, hidden_size, num_layers, output_size, batch_size=args.batch_size).to(device)
    # models = LSTM(input_size, hidden_size, num_layers, output_size, batch_size=args.batch_size).to(device)
    model.load_state_dict(torch.load(path)['models'])
    model.eval()
    print('predicting...')
    for (seq, target) in tqdm(Dte):
        target = list(chain.from_iterable(target.data.tolist()))
        y.extend(target)
        seq = seq.to(device)
        with torch.no_grad():
            y_pred = model(seq)
            y_pred = list(chain.from_iterable(y_pred.data.tolist()))
            pred.extend(y_pred)

    y, pred = np.array(y), np.array(pred)
    y = (m - n) * y + n
    pred = (m - n) * pred + n
    print('mape:', get_mape(y, pred))
    # plot
    x = [i for i in range(1, 151)]
    x_smooth = np.linspace(np.min(x), np.max(x), 900)
    y_smooth = make_interp_spline(x, y[150:300])(x_smooth)
    plt.plot(x_smooth, y_smooth, c='green', marker='*', ms=1, alpha=0.75, label='true')

    y_smooth = make_interp_spline(x, pred[150:300])(x_smooth)
    plt.plot(x_smooth, y_smooth, c='red', marker='o', ms=1, alpha=0.75, label='pred')
    plt.grid(axis='y')
    plt.legend()
    plt.show()
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简单训练30轮，MAPE为5.77%：
在这里插入图片描述

VI. 源码及数据

暂无。

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