700字范文 > PyTorch搭建LSTM实现时间序列预测（负荷预测）

PyTorch搭建LSTM实现时间序列预测（负荷预测）

时间：2020-12-05 02:37:22

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：

深入理解PyTorch中LSTM的输入和输出（从input输入到Linear输出）PyTorch搭建LSTM实现时间序列预测（负荷预测）PyTorch搭建LSTM实现多变量时间序列预测（负荷预测）PyTorch搭建双向LSTM实现时间序列预测（负荷预测）PyTorch搭建LSTM实现多变量多步长时间序列预测（一）：直接多输出PyTorch搭建LSTM实现多变量多步长时间序列预测（二）：单步滚动预测PyTorch搭建LSTM实现多变量多步长时间序列预测（三）：多模型单步预测PyTorch搭建LSTM实现多变量多步长时间序列预测（四）：多模型滚动预测PyTorch搭建LSTM实现多变量多步长时间序列预测（五）：seq2seqPyTorch中实现LSTM多步长时间序列预测的几种方法总结（负荷预测）PyTorch-LSTM时间序列预测中如何预测真正的未来值PyTorch搭建LSTM实现多变量输入多变量输出时间序列预测（多任务学习）PyTorch搭建ANN实现时间序列预测（风速预测）PyTorch搭建CNN实现时间序列预测（风速预测）PyTorch搭建CNN-LSTM混合模型实现多变量多步长时间序列预测（负荷预测）PyTorch搭建Transformer实现多变量多步长时间序列预测（负荷预测）PyTorch时间序列预测系列文章总结（代码使用方法）TensorFlow搭建LSTM实现时间序列预测（负荷预测）TensorFlow搭建LSTM实现多变量时间序列预测（负荷预测）TensorFlow搭建双向LSTM实现时间序列预测（负荷预测）TensorFlow搭建LSTM实现多变量多步长时间序列预测（一）：直接多输出TensorFlow搭建LSTM实现多变量多步长时间序列预测（二）：单步滚动预测TensorFlow搭建LSTM实现多变量多步长时间序列预测（三）：多模型单步预测TensorFlow搭建LSTM实现多变量多步长时间序列预测（四）：多模型滚动预测TensorFlow搭建LSTM实现多变量多步长时间序列预测（五）：seq2seqTensorFlow搭建LSTM实现多变量输入多变量输出时间序列预测（多任务学习）TensorFlow搭建ANN实现时间序列预测（风速预测）TensorFlow搭建CNN实现时间序列预测（风速预测）TensorFlow搭建CNN-LSTM混合模型实现多变量多步长时间序列预测（负荷预测）

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.columnsdf.fillna(df.mean(), inplace=True)return dfclass MyDataset(Dataset):def __init__(self, data):self.data = datadef __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()# splittrain = 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 seqDtr = process(train, B, True)Val = process(val, B, True)Dte = process(test, B, False)return Dtr, Val, Dte, m, n

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_sizeself.hidden_size = hidden_sizeself.num_layers = num_layersself.output_size = output_sizeself.num_directions = 1 # 单向LSTMself.batch_size = batch_sizeself.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

IV. 训练

def train(args, Dtr, Val, path):input_size, hidden_size, num_layers = args.input_size, args.hidden_size, args.num_layersoutput_size = args.output_sizeif 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)# trainingmin_epochs = 10best_model = Nonemin_val_loss = 5for 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()# validationval_loss = get_val_loss(args, model, Val)if epoch > min_epochs and val_loss < min_val_loss:min_val_loss = val_lossbest_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)

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_layersoutput_size = args.output_sizeif 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 + npred = (m - n) * pred + nprint('mape:', get_mape(y, pred))# plotx = [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()

简单训练30轮，MAPE为5.77%：