DL|PyTorch搭建ANN实现时间序列预测（风速预测） Practice|pytorch|ANN|时间序列预测

数据集
特征构造
数据处理
- 1.数据预处理
- 2.数据集构造
ANN模型
- 1.模型训练
- 2.模型预测及表现

数据集

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数据集为Barcelona某段时间内的气象数据，其中包括温度、湿度以及风速等。本文将简单搭建来对风速进行预测。
特征构造对于风速的预测，除了考虑历史风速数据外，还应该充分考虑其余气象因素的影响。因此，我们根据前24个时刻的风速+下一时刻的其余气象数据来预测下一时刻的风速。
数据处理 1.数据预处理数据预处理阶段，主要将某些列上的文本数据转为数值型数据，同时对原始数据进行归一化处理。文本数据如下所示：

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经过转换后，上述各个类别分别被赋予不同的数值，比如"sky is clear"为0，"few clouds"为1。

def load_data(): global Max, Min df = pd.read_csv('Barcelona/Barcelona.csv') df.drop_duplicates(subset=[df.columns[0]], inplace=True) # weather_main listType = df['weather_main'].unique() df.fillna(method='ffill', inplace=True) dic = dict.fromkeys(listType) for i in range(len(listType)): dic[listType[i]] = i df['weather_main'] = df['weather_main'].map(dic) # weather_description listType = df['weather_description'].unique() dic = dict.fromkeys(listType) for i in range(len(listType)): dic[listType[i]] = i df['weather_description'] = df['weather_description'].map(dic) # weather_icon listType = df['weather_icon'].unique() dic = dict.fromkeys(listType) for i in range(len(listType)): dic[listType[i]] = i df['weather_icon'] = df['weather_icon'].map(dic) # print(df)columns = df.columns Max = np.max(df['wind_speed'])# 归一化 Min = np.min(df['wind_speed'])for i in range(2, 17): column = columns[i] if column == 'wind_speed': continue df[column] = df[column].astype('float64') if len(df[df[column] == 0]) == len(df):# 全0 continue mx = np.max(df[column]) mn = np.min(df[column]) df[column] = (df[column] - mn) / (mx - mn) # print(df.isna().sum()) return df

2.数据集构造利用当前时刻的气象数据和前24个小时的风速数据来预测当前时刻的风速：

def nn_seq(): """ :param flag: :param data: 待处理的数据 :return: X和Y两个数据集,X=[当前时刻的year，month, hour, day, lowtemp, hightemp, 前一天当前时刻的负荷以及前23小时负荷] Y=[当前时刻负荷] """ print('处理数据:') data = https://www.it610.com/article/load_data() speed = data['wind_speed'] speed = speed.tolist() speed = torch.FloatTensor(speed).view(-1) data = https://www.it610.com/article/data.values.tolist() seq = [] for i in range(len(data) - 30): train_seq = [] train_label = [] for j in range(i, i + 24): train_seq.append(speed[j]) # 添加温度、湿度、气压等信息 for c in range(2, 7): train_seq.append(data[i + 24][c]) for c in range(8, 17): train_seq.append(data[i + 24][c]) train_label.append(speed[i + 24]) train_seq = torch.FloatTensor(train_seq).view(-1) train_label = torch.FloatTensor(train_label).view(-1) seq.append((train_seq, train_label)) # print(seq[:5])Dtr = seq[0:int(len(seq) * 0.5)] Den = seq[int(len(seq) * 0.50):int(len(seq) * 0.75)] Dte = seq[int(len(seq) * 0.75):len(seq)]return Dtr, Den, Dte

任意输出其中一条数据：

(tensor([1.0000e+00, 1.0000e+00, 2.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 1.0000e+00, 0.0000e+00, 1.0000e+00, 5.0000e+00, 0.0000e+00, 2.0000e+00, 0.0000e+00, 0.0000e+00, 5.0000e+00, 0.0000e+00, 2.0000e+00, 2.0000e+00, 5.0000e+00, 6.0000e+00, 5.0000e+00, 5.0000e+00, 5.0000e+00, 5.3102e-01, 5.5466e-01, 4.6885e-01, 1.0066e-03, 5.8000e-01, 6.6667e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 9.9338e-01, 0.0000e+00, 0.0000e+00, 0.0000e+00]), tensor([5.]))

数据被划分为三部分：Dtr、Den以及Dte，Dtr用作训练集，Dte用作测试集。
ANN模型 1.模型训练 ANN模型搭建如下：

def ANN(): Dtr, Den, Dte = nn_seq() my_nn = torch.nn.Sequential( torch.nn.Linear(38, 64), torch.nn.ReLU(), torch.nn.Linear(64, 128), torch.nn.ReLU(), torch.nn.Linear(128, 1), ) model = my_nn.to(device) loss_function = nn.MSELoss().to(device) optimizer = torch.optim.Adam(model.parameters(), lr=0.001) train_inout_seq = Dtr # 训练 epochs = 50 for i in range(epochs): print('当前', i) for seq, labels in train_inout_seq: seq = seq.to(device) labels = labels.to(device) y_pred = model(seq) single_loss = loss_function(y_pred, labels) optimizer.zero_grad() single_loss.backward() optimizer.step() # if i % 2 == 1: print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')print(f'epoch: {i:3} loss: {single_loss.item():10.10f}') state = {'model': model.state_dict(), 'optimizer': optimizer.state_dict(), 'epoch': epochs} torch.save(state, 'Barcelona' + ANN_PATH)

可以看到，模型定义的代码段为：

my_nn = torch.nn.Sequential( torch.nn.Linear(38, 64), torch.nn.ReLU(), torch.nn.Linear(64, 128), torch.nn.ReLU(), torch.nn.Linear(128, 1), )

第一层全连接层输入维度为38（前24小时风速+14种气象数据），输出维度为64；第二层输入为64，输出128；第三层输入为128，输出为1。
2.模型预测及表现

def ANN_predict(ann, test_seq): pred = [] for seq, labels in test_seq: seq = seq.to(device) with torch.no_grad(): pred.append(ann(seq).item()) pred = np.array([pred])return pred

测试：

def test(): Dtr, Den, Dte = nn_seq() ann = torch.nn.Sequential( torch.nn.Linear(38, 64), torch.nn.ReLU(), torch.nn.Linear(64, 128), torch.nn.ReLU(), torch.nn.Linear(128, 1), ) ann = ann.to(device) ann.load_state_dict(torch.load('Barcelona' + ANN_PATH)['model']) ann.eval() pred = ANN_predict(ann, Dte) print(mean_absolute_error(te_y, pred2.T), np.sqrt(mean_squared_error(te_y, pred2.T)))

ANN在Dte上的表现如下表所示：