周周星分享7.3—基于气象大数据的自动站实况联合预测

赛题

2024中国高校计算机大赛 — 大数据挑战赛

经验分享

大家好，我是扫地僧团队的队长，以前参加这样打榜的比赛比较少，了解的打榜技巧不是太多，所以想从科研的角度给大家一点分享。

这次比赛主要从以下五个步骤进行：数据集构造👉Baseline选择👉模型优化👉模型调参👉模型集成

1. 数据集构造

官方已经给了数据集，可以尝试根据温度筛选出与中国温度类似的场站，但是不确定是否会有效果：

import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as pltroot_path = '../dataset/global'
data_path = 'temp.npy'
data = np.load(os.path.join(root_path, data_path))data_oneyear = data[:365*24,:,0]
df = pd.DataFrame(data_oneyear)# 夏天平均温度大于15摄氏度
summer_df = df.iloc[4000:5500]
print(summer_df.shape)
summer_index = summer_df.mean(axis=0).apply(lambda x: x > 15)
summer_index = summer_index[summer_index].index.to_list()
print(len(summer_index))# 冬天平均温度小于20摄氏度
winter_df = df.iloc[0:500]
print(winter_df.shape)
winter_index = winter_df.mean(axis=0).apply(lambda x: x < 20)
winter_index = winter_index[winter_index].index.to_list()
print(len(winter_index))# 取两个表的交集
index = list(set(summer_index) & set(winter_index))
print(len(index))# 取两个表的交集
index = list(set(summer_index) & set(north_index) & set(winter_index))
print(len(index))

# 筛选电站
root_path= '../dataset/global'
temp_path = 'temp.npy'
wind_path = 'wind.npy'
global_data_path = 'global_data.npy'
temp_data = np.load(os.path.join(root_path, temp_path))
wind_data = np.load(os.path.join(root_path, wind_path))
global_data = np.load(os.path.join(root_path, global_data_path))
print(temp_data.shape)
print(wind_data.shape)
print(global_data.shape)temp_seleted = temp_data[:,index,:]
wind_seleted = wind_data[:,index,:]
global_seleted = global_data[:,:,:,index]
print(temp_seleted.shape)
print(wind_seleted.shape)
print(global_seleted.shape)# 划分训练集和验证集
l = temp_seleted.shape[0]
train_size = int(l * 0.9)
temp_seleted_train = temp_seleted[:train_size,:,:]
wind_seleted_train = wind_seleted[:train_size,:,:]
global_seleted_train = global_seleted[:int(train_size/3),:,:]
temp_seleted_val = temp_seleted[train_size:,:,:]
wind_seleted_val = wind_seleted[train_size:,:,:]
global_seleted_val = global_seleted[int(train_size/3):,:,:]
print("train:",temp_seleted_train.shape,wind_seleted_train.shape,global_seleted_train.shape)
print("val:",temp_seleted_val.shape,wind_seleted_val.shape,global_seleted_val.shape)# 保存训练集和验证集
if not os.path.exists(os.path.join('../dataset', 'seleted_global_train_val')):os.makedirs(os.path.join('../dataset', 'seleted_global_train_val'))
selected_path = os.path.join('../dataset', 'seleted_global_train_val')
np.save(os.path.join(selected_path, 'temp_train.npy'), temp_seleted_train)
np.save(os.path.join(selected_path, 'temp_val.npy'), temp_seleted_val)
np.save(os.path.join(selected_path, 'wind_train.npy'), wind_seleted_train)
np.save(os.path.join(selected_path, 'wind_val.npy'), wind_seleted_val)
np.save(os.path.join(selected_path, 'global_train.npy'), global_seleted_train)
np.save(os.path.join(selected_path, 'global_val.npy'), global_seleted_val)

筛选后温度和风速形状如图所示：

2. Baseline选择

官方Baseline给的是iTransformer，关于iTransformer模型的解读请参考：【PaperInFive-时间序列预测】iTransformer:转置Transformer刷新时间序列预测SOTA(清华)

可以关注近近两年开源的SOTA模型，这里分享一个Github，可以去上面找近年的SOTA模型：https://github.com/ddz16/TSFpaper

3. 模型优化

选好效果好的Baseline后就可以进行模型优化，比如iTransformer只建模了特征信息，那么可以在模型中补充对时序特征的建模，比如进行一些卷积操作，或者在时间维度上进行self-Attention，关于时间维度上的建模大家也可以参考SOTA论文，可以把不同论文里的模块进行一个融合，说不定会有好效果。

4. 模型调参

确定了模型结构后就可以进行模型超参数的调整，比如模型的维度和层数，学习率和batch size等，经过测试增加模型的dimention在一定程度上可以提高模型表现，但是增加层数好像效果不太明显。

学习率方面我初始值为0.01或0.005，每一轮除以2进行衰减。batch size我设为40960。

5. 模型集成

最后可以把不同特征的模型进行集成，比如可以把多个模型的结果取平均，或者可以在训练时采用Mixture of Expert的方式加权求和。

帮助代码

1. 模型测试

加在exp_long_term_forecasting.py里面：

    def val(self, setting):_, _, val_data, val_loader = self._get_data()time_now = time.time()criterion = self._select_criterion()if self.args.use_amp:scaler = torch.cuda.amp.GradScaler()self.model.load_state_dict(torch.load(self.args.state_dict_path,map_location=torch.device('cuda:0')))self.model.eval()val_loss = []for i, (batch_x, batch_y) in enumerate(val_loader):batch_x = batch_x.float().to(self.device)batch_y = batch_y.float().to(self.device)# encoder - decoderif self.args.use_amp:with torch.cuda.amp.autocast():if self.args.output_attention:outputs = self.model(batch_x)[0]else:outputs = self.model(batch_x)f_dim = -1 if self.args.features == 'MS' else 0outputs = outputs[:, -self.args.pred_len:, f_dim:]batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)loss = criterion(outputs, batch_y)print("\titers: {0} | loss: {2:.7f}".format(i + 1, loss.item()))val_loss.append(loss.item())else:if self.args.output_attention:outputs = self.model(batch_x)[0]else:outputs = self.model(batch_x)f_dim = -1 if self.args.features == 'MS' else 0outputs = outputs[:, -self.args.pred_len:, f_dim:]batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)loss = criterion(outputs, batch_y)if (i + 1) % 50 == 0:print("\titers: {0} | loss: {1:.7f}".format(i + 1, loss.item()))val_loss.append(loss.item())val_loss = np.average(val_loss)print("Val Loss: {0:.7f}".format(val_loss))return self.model

2. 验证集Dataloader

加在data_factory.py里面：

def data_provider(args):Data = data_dict[args.data]shuffle_flag = Truedrop_last = Falsebatch_size = args.batch_size train_data_set = Data(root_path=args.root_path,data_path=args.train_data_path,global_path=args.train_global_path,size=[args.seq_len, args.label_len, args.pred_len],features=args.features)train_data_loader = DataLoader(train_data_set,batch_size=batch_size,shuffle=shuffle_flag,num_workers=args.num_workers,drop_last=drop_last)val_data_set = Data(root_path=args.root_path,data_path=args.val_data_path,global_path=args.val_global_path,size=[args.seq_len, args.label_len, args.pred_len],features=args.features)val_data_loader = DataLoader(val_data_set,batch_size=int(batch_size/8),shuffle=False,num_workers=args.num_workers,drop_last=drop_last)return train_data_set, train_data_loader, val_data_set, val_data_loader

最后

希望大家以赛为友，共同进步，一起分享一些有用的小技巧。