Python 反转对数/微分变换以进行打印 tl-dr；

如何反转以进行绘图

全文 我在反转日志换位以消除平稳性方面遇到了问题。这里是转置：

series = u[columns].copy()

endogdiffs = []
for app in endog:
    min_nonzero = series[series[app] > 0].min()[0]
    series.loc[series[app] == 0, app] = min_nonzero
    series[app + '_log'] = np.log(series[app])
    series[app + '_log_diff'] = series[app + '_log'].diff()
    endogdiffs.append(app + '_log_diff')

series = series.replace([np.inf, -np.inf], np.nan).dropna()    

那么我就在为应用程序建模。我的系列被分成训练组和测试组，预测结果被加载回一个称为y的DF

据我所知，.diff（）被.cumsum（）反转。这给了我日志。log（）由.exp（）反转

在输出方面，我认为我应该这样绘制：

绘制输出 但是——我的初始值（所有的，不仅仅是内生的）都在0-1之间。我的输出值大约是1-50或60左右的y预测值

如何反转变换

预测部分的详细信息：

训练并运行模型

series = u[columns].copy()

endogdiffs = []
for app in endog:
    min_nonzero = series[series[app] > 0].min()[0]
    series.loc[series[app] == 0, app] = min_nonzero
    series[app + '_log'] = np.log(series[app])
    series[app + '_log_diff'] = series[app + '_log'].diff()
    endogdiffs.append(app + '_log_diff')

series = series.replace([np.inf, -np.inf], np.nan).dropna()    

for i, app in enumerate(endog):
    plt.plot(np.exp(train[app + '_log_diff'].append(y[app + '_log_diff']).cumsum()), color=[(i/10)+0.5, (i/10)+0.5, (i/10)+0.5])
    plt.plot(np.exp(train[app + '_log_diff'].append(test[app + '_log_diff']).cumsum()), color=appColors[i])

train, test = series[:size], series[size:size+(28*4*24)]

train = train.loc[:, (train != train.iloc[0]).any()] # https://stackoverflow.com/questions/20209600/panda-dataframe-remove-constant-column
test = test.loc[:, (test != test.iloc[0]).any()]
#print(train.var(), X.info())

# train autoregression
model = VARMAX(train[endogdiffs], exog=train[exog])

model_fit = model.fit(model='cg')
#print(model_fit.mle_retvals)

model_fit.plot_diagnostics()

##window = model_fit.k_ar
coef = model_fit.params

predictions = pd.DataFrame()
predictions = model_fit.forecast(steps=len(test), exog=test[exog])
y = predictions.copy()