SummaryofRegressionResults === Model:VAR Method:OLS Date:Fri,30,Jun,2023 Time:15:21:42 --- No.ofEquations:2.00000BIC:-18.0274 Nobs:6573.00HQIC:-18.0558 Loglikelihood:40778.3FPE:1.41889e-08 AIC:-18.0708Det(Omega_mle):1.40986e-08 --- Resultsforequation深证...
data.head())# print('Values:\n', data)# === Step 2.1: Normalize Data (0-1) ===#data, normalize_modele = normalize_regression(data, type_normalize='MinMaxScaler', display_figure='on') # Type_Normalize: 'MinMaxScaler', 'nor...
def draw_trend(timeseries, size): ''' 绘制时间序列趋势线,size是移动平均的趋势。绘制原始趋势及移动平均的水平和波动 ''' plt.style.use('seaborn') plt.rcParams['font.sans-serif']=['Heiti TC'] plt.rcParams['axes.unicode_minus'] = False plt.rcParams.update({'font.size': 12}) f = plt...
parse_dates=['date'])detrended = signal.detrend(df.value.values) #用于去趋势化(detrend)#df.value 返回的是一个 pandas Series 对象,它代表了 DataFrame 中名为 'value' 的列。#
作为本课程的第一部分,我们将使用线性回归来构建预测模型。线性回归在实践中被广泛使用,即使复杂的预测任务也能自然的适应(Linear regression is widely used in practice and adapts naturally to even complex forecasting tasks.)。 线性回归学习如何从其输入特征中得出加权和。 对于两个特征,我们将有: ...
model = LinearRegression() model.fit(X, y) # Store the fitted values as a time series with the same time index as # the training data y_pred = pd.Series(model.predict(X), index=X.index) 1. 2. 3. 4. 5. 6. 7. 8.
(data,index=dates,columns=['Value']).reset_index()# 使用Seaborn绘制带有回归线的时间序列图plt.figure(figsize=(10,6))sns.regplot(x='index',y='Value',data=df,scatter_kws={'s':10},line_kws={'color':'red'})plt.title('Time Series Data with Regression Line')plt.xlabel('Date')plt....
# KPSS Testresult = kpss(df.value.values, regression='c')print('\nKPSS Statistic: %f' % result[0])print('p-value: %f' % result[1])for key, value in result[3].items():print('Critial Values:')print(f' {key}, {value}')...
# 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):plt.figure(...
def KPSS_test(timeseries): print("Results of KPSS Test:") kpsstest = kpss(timeseries.dropna(), regression="c", nlags="auto") kpss_output = pd.Series( kpsstest[0:3], index=["Test Statistic", "p-value", "Lags Used"] )