执行主成分分析以重建时间序列会产生比预期更多的值

如何解决执行主成分分析以重建时间序列会产生比预期更多的值

我想在 this notebook 之后进行主成分分析，以从其成分（通过 Quandl 找到）重建 DJIA（我正在使用 alpha_ventage）。然而，在重建将主成分与其权重相乘的值时，我似乎创建了比原始数据帧更多的值

kernel_pca = KernelPCA(n_components=5).fit(df_z_components)
pca_5 = kernel_pca.transform(-daily_df_components)

weights = fn_weighted_average(kernel_pca.lambdas_)
reconstructed_values = np.dot(pca_5,weights)

实际上，daily_df_components 是通过 quandl API 从 DJIA 的组件创建的，它似乎比我用来获取 DJIA 指数的库 alpha_ventage 拥有更多的数据。

这是完整的代码

"""
Obtaining the components data from quandl
"""
import quandl

QUANDL_API_KEY = 'MYKEY'
quandl.ApiConfig.api_key = QUANDL_API_KEY

SYMBOLS = [
        'AAPL','MMM','BA','AXP','CAT','CVX','CSCO','KO','DD','XOM','GS','HD','IBM','INTC','JNJ','JPM','MCD','MRK','MSFT','NKE','PFE','PG','UNH','UTX','TRV','VZ','V','WMT','WBA','DIS'
]

wiki_symbols = ['WIKI/%s'%symbol for symbol in SYMBOLS]
df_components = quandl.get(
    wiki_symbols,start_date='2017-01-01',end_date='2017-12-31',column_index=11)
df_components.columns = SYMBOLS

filled_df_components = df_components.fillna(method='ffill')
daily_df_components = filled_df_components.resample('24h').ffill()
daily_df_components = daily_df_components.fillna(method='bfill')


"""
Download the all-time DJIA dataset
"""
from alpha_vantage.timeseries import TimeSeries

# Update your Alpha Vantage API key here...
ALPHA_VANTAGE_API_KEY = 'MYKEY'

ts = TimeSeries(key=ALPHA_VANTAGE_API_KEY,output_format='pandas')
df,meta_data = ts.get_intraday(symbol='DIA',interval='1min',outputsize='full')

# Finding eigenvectors and eigen values
fn_weighted_average = lambda x: x/x.sum()
weighted_values = fn_weighted_average(fitted_pca.lambdas_)[:5]

from sklearn.decomposition import KernelPCA

fn_z_score = lambda x: (x - x.mean())/x.std()

df_z_components = daily_df_components.apply(fn_z_score)
fitted_pca = KernelPCA().fit(df_z_components)

# Reconstructing the Dow Average with PCA
import numpy as np

kernel_pca = KernelPCA(n_components=5).fit(df_z_components)
pca_5 = kernel_pca.transform(-daily_df_components)

weights = fn_weighted_average(kernel_pca.lambdas_)
reconstructed_values = np.dot(pca_5,weights)

# Combine PCA and Index to compare
df_combined = djia_2020_weird.copy()
df_combined['pca_5'] = reconstructed_values

但它返回：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-100-2808dc14f789> in <module>()
      9 # Combine PCA and Index to compare
     10 df_combined = djia_2020_weird.copy()
---> 11 df_combined['pca_5'] = reconstructed_values
     12 df_combined = df_combined.apply(fn_z_score)
     13 df_combined.plot(figsize=(12,8));

3 frames
/usr/local/lib/python3.6/dist-packages/pandas/core/internals/construction.py in sanitize_index(data,index)
    746     if len(data) != len(index):
    747         raise ValueError(
--> 748             "Length of values "
    749             f"({len(data)}) "
    750             "does not match length of index "

ValueError: Length of values (361) does not match length of index (14)

确实，reconstructed_values 是 361 长而 df_combined 是 14 值长... 这是最后一个数据框：

            DJI
date    
2021-01-21  NaN
2021-01-22  311.37
2021-01-23  310.03
2021-01-24  310.03
2021-01-25  310.03
2021-01-26  309.01
2021-01-27  309.49
2021-01-28  302.17
2021-01-29  305.25
2021-01-30  299.20
2021-01-31  299.20
2021-02-01  299.20
2021-02-02  302.13
2021-02-03  307.86

可能是notebook作者可以拿到他感兴趣的一整年的数据，我跑数据的时候好像只有两个月？