AI检测代码解析 importnumpyasnpimportpandasaspdimportmatplotlib.pyplotasplt# 生成时间序列数据defgenerate_time_series_data(num_points=100):time=np.arange(0,num_points)data=np.sin(time*0.1)+np.random.normal(scale=0.1,size=num_points)# 加入了一些噪声returntime,data# 获取数据time,data=generate_time_...
为了衡量缺失值的表现,我在时间序列当中手动引入缺失值,使用上述方法处理并衡量处理值和真实值之间的均方误差。 # 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'...
AI代码解释 # Time series data source:fpp pacakgeinR.importmatplotlib.pyplotasplt df=pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv',parse_dates=['date'],index_col='date')# Draw Plot defplot_df(df,x,y,title="",xlabel='Date',ylabel='Value',dpi=100):...
TimeSeriesGenerator+generate_time_series(start_date, end_date) 4. 旅行图 Data Preparation TimeSeriesGenerator->start_date TimeSeriesGenerator->end_date Data Processing TimeSeriesGenerator->pd.date_range() Visualization TimeSeriesGenerator->plt.plot() ...
ts.shift(2,freq='M')#偏移2月ts.shift(3, freq="D")#偏移3天ts.shift(1, freq="90T")#偏移1个半小时frompandas.tseries.offsetsimportDay, MonthEnd now= datetime(2011, 11, 17) now+ 3 * Day()#加3天#Timestamp('2011-11-20 00:00:00')now+ MonthEnd()##日期滚动到月底Timestamp('201...
m=SARIMAModel(data=air_passengers_ts,params=params)# fitSARIMAmodel m.fit()# generate forecast values fcst=m.predict(steps=30,freq="MS")m.plot() 预测库比较 这里提供了一个包含一些常见功能的表格来比较预测包。表中显示了一些指标,例如GitHub...
timeSeries.plot(color='blue') plt.show()'''Unit Root Test The null hypothesis of the Augmented Dickey-Fuller is that there is a unit root, with the alternative that there is no unit root. That is to say the bigger the p-value the more reason we assert that there is a unit root'...
# Time series data source: fpp pacakge in R.import matplotlib.pyplot as pltdf = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/a10.csv', parse_dates=['date'], index_col='date') # Draw Plotdef plot_df(...
end_date) generator.generate_random_data() result_data = generator.get_data() for date, value...
What Are Anomalies in a Time Series?# Generate the data import numpy as np import matplotlib.pyplot as plt # A time series mu, sigma = 0, 0.1 # mean and standard deviation obs = 300 plt.figure(figsi…