# 生成随机数 from scipy.interpolate import interp1d from sklearn.metrics import mean_squared_error df_orig = pd.read_csv("../datasets/a10.csv", parse_dates=['date'], index_col=['date']) # 制作缺失值数据 np.random.seed(2021) df = df_orig.copy() df.iloc[np.random.randint(low=0...
from scipy.interpolate import CubicSpline from datetime import datetime, timedelta from PyEMD import EMD, EEMD, CEEMDAN import warnings warnings.simplefilter(action='ignore', category=FutureWarning) def get_stock_data(ticker, start_date, end_date): try: data = yf.download(ticker, start=start_date...
时间插值(适合时间序列数据): # 创建一个时间序列time_series_data=pd.Series([1,2,np.nan,4,5,np.nan,7],index=pd.date_range('2023-01-01',periods=7))# 使用时间插值填补缺失值time_interpolated=time_series_data.interpolate(method='time')print("\n时间插值后的数据:")print(time_interpolated) ...
ts = pd.Series([1, None, 3, None, 5], index=pd.date_range('2023-04-01', periods=5, freq='D')) # 使用线性插值方法填充缺失值 ts_interpolated = ts.interpolate() print("原始数据:\n", ts) print("\n线性插值填充后的数据:\n", ts_interpolated) 1. 2. 3. 4. 5. 6. 7. 8. ...
使用Python拆分TimeSeries数据的方法有很多种,以下是其中一种常见的方法: 1. 首先,确保你已经安装了Python的pandas库,它提供了强大的数据处理和分析功能。 2. 导入所需...
# # Generate datasetfrom scipy.interpolate import interp1dfrom sklearn.metrics import mean_squared_errordf_orig = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date'], index_col='date').head(100)df = pd.read_csv('datasets/a10_missings...
# # Generate datasetfrom scipy.interpolate import interp1dfrom sklearn.metrics import mean_squared_errordf_orig = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date'], index_col='date...
1.time_series_fill.py # -*- coding:utf-8 -*- from pandas.compat import reduce __author__ = 'gin.chen' import pandas as pd import numpy as np import matplotlib.pyplot as plt from scipy.interpolate import lagrange # from fbprophet import Prophet ...
此数据集是 Monash Time Series Forecasting 存储库的一部分,该存储库收纳了是来自多个领域的时间序列数据集。它可以看作是时间序列预测的 GLUE 基准。 from datasets import load_datasetdataset = load_dataset("monash_tsf", "tourism_monthly") 可以看出,数据集包...
7, 8]}dates = pd.date_range(start='2021-01-01', periods=len(data))df_time_series = pd.DataFrame(data, index=dates)# 使用时间线性插值填充缺失值df_time_series_interpolated = df_time_series.interpolate(method='time')print("使用时间线性插值填充缺失值的结果:\n", df_time_series_interpolated...