我们可以在 Python 中对多元时间序列数据集进行聚类吗

Question

我有一个数据集，其中包含不同股票在不同时间的许多金融信号值。例如

StockName  Date   Signal1  Signal2
----------------------------------
Stock1     1/1/20    a       b
Stock1     1/2/20    c       d
.
.
.
Stock2     1/1/20    e       f
Stock2     1/2/20    g       h
.
.
.

我想建立一个如下所示的时间序列表，并根据信号 1 和信号 2（2 个变量）对股票进行聚类

StockName   1/1/20    1/2/20    ........    Cluster#
----------------------------------------------------
 Stock1     [a,b]      [c,d]                    0
 Stock2     [e,f]      [g,h]                    1
 Stock3     ......     .....                    0
 .
 .
 .

1)有什么方法可以做到这一点吗？ （基于时间序列数据的多个变量对股票进行聚类）。我尝试在线搜索，但它们都是关于基于一个变量的聚类时间序列。

2)另外，有没有办法在不同的时间对不同的股票进行聚类？ （所以也许时间 1 的 Stock1 与时间 3 的 Stock2 在同一个集群中）

Answer 1

值得阅读的好材料（标题：时间序列聚类和降维）

https://towardsdatascience.com/time-series-clustering-and-dimensionality-reduction-5b3b4e84f6a3

Answer 2

我正在根据您上次发布的新信息在这里修改我的答案。

from utils import *

import time
import numpy as np

from mxnet import nd, autograd, gluon
from mxnet.gluon import nn, rnn
import mxnet as mx
import datetime
import seaborn as sns
import matplotlib.pyplot as plt

# %matplotlib inline
from sklearn.decomposition import PCA

import math

from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler

import xgboost as xgb
from sklearn.metrics import accuracy_score

import warnings
warnings.filterwarnings("ignore")

context = mx.cpu(); model_ctx=mx.cpu()
mx.random.seed(1719)

# Note: The purpose of this section (3. The Data) is to show the data preprocessing and to give rationale for using different sources of data, hence I will only use a subset of the full data (that is used for training).

def parser(x):
    return datetime.datetime.strptime(x,'%Y-%m-%d')

# dataset_ex_df = pd.read_csv('data/panel_data_close.csv', header=0, parse_dates=[0], date_parser=parser)


import yfinance as yf

# Get the data for the stock AAPL
start = '2018-01-01'
end = '2020-04-22'

data = yf.download('GS', start, end)

data = data.reset_index()
data

    data.dtypes

    # re-name field from 'Adj Close' to 'Adj_Close'
    data = data.rename(columns={"Adj Close": "Adj_Close"})
    data

num_training_days = int(data.shape[0]*.7)
print('Number of training days: {}. Number of test days: {}.'.format(num_training_days, data.shape[0]-num_training_days))



# TECHNICAL INDICATORS
#def get_technical_indicators(dataset):
# Create 7 and 21 days Moving Average
data['ma7'] = data['Adj_Close'].rolling(window=7).mean()
data['ma21'] = data['Adj_Close'].rolling(window=21).mean()


# Create exponential weighted moving average
data['26ema'] = data['Adj_Close'].ewm(span=26).mean()
data['12ema'] = data['Adj_Close'].ewm(span=12).mean()
data['MACD'] = (data['12ema']-data['26ema'])

# Create Bollinger Bands
data['20sd'] = data['Adj_Close'].rolling(window=20).std() 
data['upper_band'] = data['ma21'] + (data['20sd']*2)
data['lower_band'] = data['ma21'] - (data['20sd']*2)

# Create Exponential moving average
data['ema'] = data['Adj_Close'].ewm(com=0.5).mean()

# Create Momentum
data['momentum'] = data['Adj_Close']-1



dataset_TI_df = data
dataset = data


def plot_technical_indicators(dataset, last_days):
    plt.figure(figsize=(16, 10), dpi=100)
    shape_0 = dataset.shape[0]
    xmacd_ = shape_0-last_days

    dataset = dataset.iloc[-last_days:, :]
    x_ = range(3, dataset.shape[0])
    x_ =list(dataset.index)

    # Plot first subplot
    plt.subplot(2, 1, 1)
    plt.plot(dataset['ma7'],label='MA 7', color='g',linestyle='--')
    plt.plot(dataset['Adj_Close'],label='Closing Price', color='b')
    plt.plot(dataset['ma21'],label='MA 21', color='r',linestyle='--')
    plt.plot(dataset['upper_band'],label='Upper Band', color='c')
    plt.plot(dataset['lower_band'],label='Lower Band', color='c')
    plt.fill_between(x_, dataset['lower_band'], dataset['upper_band'], alpha=0.35)
    plt.title('Technical indicators for Goldman Sachs - last {} days.'.format(last_days))
    plt.ylabel('USD')
    plt.legend()

    # Plot second subplot
    plt.subplot(2, 1, 2)
    plt.title('MACD')
    plt.plot(dataset['MACD'],label='MACD', linestyle='-.')
    plt.hlines(15, xmacd_, shape_0, colors='g', linestyles='--')
    plt.hlines(-15, xmacd_, shape_0, colors='g', linestyles='--')
    # plt.plot(dataset['log_momentum'],label='Momentum', color='b',linestyle='-')

    plt.legend()
    plt.show()

plot_technical_indicators(dataset_TI_df, 400)

这将为您提供一些可以使用的信号。当然，这些功能可以是您想要的任何东西。我相信你知道这是技术分析，而不是基本面分析。现在，您可以进行聚类，以及您想要的任何其他内容。

这是一个很好的聚类链接。

https://www.pythonforfinance.net/2018/02/08/stock-clusters-using-k-means-algorithm-in-python/