29、时间序列预测与风格迁移：从数据预测到图像风格转换

时间序列预测与风格迁移：从数据预测到图像风格转换

1. 时间序列预测

1.1 数据准备

首先，我们需要对数据进行处理，将其转换为适合模型训练的格式。以下是具体的代码实现：

import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import sklearn.preprocessing
import seaborn as sns

url = 'https://raw.githubusercontent.com/Apress/artificial-neural-networks-with-tensorflow-2/main/ch11/london_merged.csv' 
df = pd.read_csv(url, parse_dates=['timestamp'], index_col="timestamp")

# 检查平稳性
from statsmodels.tsa.vector_ar.vecm import coint_johansen
johan_test_temp = df
coint_johansen(johan_test_temp, -1, 1).eig

# 绘制数据
plt.figure(figsize = (16,4))
plt.plot(df.index, df["cnt"]);

# 创建索引
df['hour'] = df.index.hour
df['month'] = df.index.month

# 绘制不同特征与目标值的关系图
fig, (ax1,