A visual enhancement for Arma 3 using ReShade and in-engine post processing. I aim to provide high quality realistic graphics by making corrections to color, brightness, ambient occlusion, lighting, and other post processing. 879KB 55 5.6k Project Titanfall Titans Mod pack Uploaded: 03 Nov...
ts_ax.set_title('Time Series Analysis Plots') smt.graphics.plot_acf(y, lags=lags, ax=acf_ax, alpha=0.05) smt.graphics.plot_pacf(y, lags=lags, ax=pacf_ax, alpha=0.05) sm.qqplot(y, fit=True,line='45', ax=qq_ax) qq_ax.set_title('QQ Plot') scs.probplot(y, sparams=(y.mea...
系数p值均小于0.05,说明系数均显著不为0。如果存在某个系数的p值较大,说明t检验中系数不显著,贡献不大,可以剔除,减小模型冗余度。 模型检验 可视化残差直方图、QQ图,看是否正态分布,可视化ACF看是否仍存有自相关性。 import statsmodels.api as sm from statsmodels.graphics.tsaplots import plot_acf fig, axs ...
4.2.1. 绘制自相关、偏自相关图 statsmodels.graphics.tsa.plot_acf(),绘制时间序列的自相关图。 statsmodels.graphics.tsa.plot_pacf(),绘制时间序列的偏自相关图。 其中,参数(x, ax=None, lags=None, alpha=0.05, use_vlines=True, unbiased=False, fft=False, title=‘Autocorrelation’, zero=True, **...
import statsmodels.api as sm from statsmodels.graphics.tsaplots import plot_acf fig, axs = plt.subplots(2, 2) fig.subplots_adjust(hspace=0.3) model.resid.plot(ax=axs[0][0]) axs[0][0].set_title('residual') model.resid.plot(kind='hist', ax=axs[0][1]) ...
importstatsmodels.apiassm fromstatsmodels.graphics.tsaplotsimportplot_acf fig, axs = plt.subplots(2,2) fig.subplots_adjust(hspace=0.3) model.resid.plot(ax=axs[0][0]) axs[0][0].set_title('residual') model.resid.plot(kind='hist', ax=axs[0][1]) ...
('Time Series Analysis Plots')smt.graphics.plot_acf(y,lags=lags,ax=acf_ax,alpha=0.05)smt.graphics.plot_pacf(y,lags=lags,ax=pacf_ax,alpha=0.05)sm.qqplot(y,line='s',ax=qq_ax)qq_ax.set_title('QQ Plot')scs.probplot(y,sparams=(y.mean(),y.std()),plot=pp_ax)plt.tight_...
importstatsmodels.apiassm fromstatsmodels.graphics.tsaplotsimportplot_acf fig, axs = plt.subplots(2,2) fig.subplots_adjust(hspace=0.3) model.resid.plot(ax=axs[0][0]) axs[0][0].set_title('residual') model.resid.plot(kind='hist', ax=axs[0][1]) ...
graphics.tsaplots import plot_pacf as PACF #偏自相关图 fig = PACF(r2,lags = 30) #使用对数收益率序列 plt.show() 可以看出1阶之后基本接近0定阶1或者6或者26 2. 信息准则— AIC、BIC、HQ 给出的结果!=0!=1 使用其否则根据PACF图形的结果进行尝试 现在有以上这么多可供选择的模型我们通常采用AIC...
smt.graphics.plot_acf(y, lags=lags, ax=acf_ax, alpha=0.05) smt.graphics.plot_pacf(y, lags=lags, ax=pacf_ax, alpha=0.05) sm.qqplot(y, fit=True,line='45', ax=qq_ax) qq_ax.set_title('QQ Plot') scs.probplot(y, sparams=(y.mean(), y.std()), plot=pp_ax) ...