在Python中对时间序列数据进行聚类

import pandas as pd

# 示例数据
data = {
    'timestamp': ['2020-01-01', '2020-01-02', '2020-01-01'],
    'value': [10, 20, 30]
}
df = pd.DataFrame(data)
df['timestamp'] = pd.to_datetime(df['timestamp'])
df.set_index('timestamp', inplace=True)

# 重采样
df_resampled = df.resample('D').mean().fillna(method='ffill')
print(df_resampled)

from sklearn.decomposition import PCA

# 示例数据
data = pd.DataFrame({
    'feature1': [1, 2, 3, 4, 5],
    'feature2': [5, 4, 3, 2, 1]
})

# PCA降维
pca = PCA(n_components=1)
reduced_data = pca.fit_transform(data)
print(reduced_data)

from sklearn.cluster import KMeans

# 示例数据
data = pd.DataFrame({
    'feature1': [1, 2, 3, 4, 5],
    'feature2': [5, 4, 3, 2, 1]
})

# K-means聚类
kmeans = KMeans(n_clusters=2, init='k-means++')
clusters = kmeans.fit_predict(data)
print(clusters)