pytorch cnn预测时间序列数据
时间: 2023-11-14 19:07:12 浏览: 44
在使用PyTorch进行时间序列预测时,你可以使用卷积神经网络(CNN)来处理数据。首先,你需要加载数据集并进行预处理。在预处理阶段,你可以将文本数据转换为数值型数据,并对原始数据进行归一化处理。例如,你可以使用以下代码加载和处理数据集:
```python
import pandas as pd
def load_data():
df = pd.read_csv('Barcelona/Barcelona.csv')
df.drop_duplicates(subset=\[df.columns\[0\]\], inplace=True)
df.drop(\[df.columns\[0\], df.columns\[1\]\], axis=1, inplace=True)
# 将文本数据转换为数值型数据
weather_main_types = df\['weather_main'\].unique()
weather_main_dict = dict.fromkeys(weather_main_types)
for i in range(len(weather_main_types)):
weather_main_dict\[weather_main_types\[i\]\] = i
df\['weather_main'\] = df\['weather_main'\].map(weather_main_dict)
weather_description_types = df\['weather_description'\].unique()
weather_description_dict = dict.fromkeys(weather_description_types)
for i in range(len(weather_description_types)):
weather_description_dict\[weather_description_types\[i\]\] = i
df\['weather_description'\] = df\['weather_description'\].map(weather_description_dict)
weather_icon_types = df\['weather_icon'\].unique()
weather_icon_dict = dict.fromkeys(weather_icon_types)
for i in range(len(weather_icon_types)):
weather_icon_dict\[weather_icon_types\[i\]\] = i
df\['weather_icon'\] = df\['weather_icon'\].map(weather_icon_dict)
# 进行归一化处理
df = (df - df.min()) / (df.max() - df.min())
return df
```
接下来,你可以根据问题的要求选择输入序列的长度。在这个问题中,我们选择了180天(6个月)的输入序列长度。你可以使用滑动窗口的方法来构建序列数据。最后,你可以使用CNN模型对时间序列数据进行预测。
#### 引用[.reference_title]
- *1* *2* [PyTorch 进行多步时间序列预测详细教程](https://blog.csdn.net/u010329292/article/details/129450576)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
- *3* [PyTorch搭建CNN实现时间序列预测(风速预测)](https://blog.csdn.net/Cyril_KI/article/details/122204319)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)