如何反转平稳性并将日期重新应用于数据进行绘图？
srcs：
https://nbviewer.jupyter.org/github/robbiemu/location-metric-data/blob/master/appData%20and%20locationData.ipynb
https://github.com/robbiemu/location-metric-data
我正在尝试反转平稳性并获得预测图，尤其是对于名为“ app_1”和“ app_2”的两列（下面的橙色和红色线）。
我从中提取的数据如下所示：
print(u1.info())
u1.head()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 15011 entries, 2017-08-28 11:00:00 to 2018-01-31 19:30:00
Freq: 15T
Data columns (total 10 columns):
app_1 15011 non-null float64
app_2 15011 non-null float64
user 15011 non-null object
bar 15011 non-null float64
grocers 15011 non-null float64
home 15011 non-null float64
lunch 15011 non-null float64
park 15011 non-null float64
relatives 15011 non-null float64
work 15011 non-null float64
dtypes: float64(9), object(1)
memory usage: 1.3+ MB
app_1 app_2 user bar grocers home lunch park relatives work
date
2017-08-28 11:00:00 0.010000 0.0 user_1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2017-08-28 11:15:00 0.010125 0.0 user_1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2017-08-28 11:30:00 0.010250 0.0 user_1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2017-08-28 11:45:00 0.010375 0.0 user_1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2017-08-28 12:00:00 0.010500 0.0 user_1 0.0 0.0 0.0 0.0 0.0 0.0 0.0
location列代表用户在给定时间所处的位置-在首次“重大位置更改”事件之后，一次且只有一个列一次为1。
我正在使用VARIMAX进行分析-使用statsmodels VARMAX版本的AR：
from statsmodels.tsa.statespace.varmax import VARMAX
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
from random import random
#...
columns = [ ' app_1', ' app_2', ' bar', ' grocers', ' home', ' lunch', ' work', ' park', ' relatives' ]
series = u1[columns]
# from: https://machinelearningmastery.com/make-predictions-time-series-forecasting-python/
# create a difference transform of the dataset
def difference(dataset):
diff = list()
for i in range(1, len(dataset)):
value = dataset[i] - dataset[i - 1]
diff.append(value)
return np.array(diff)
# Make a prediction give regression coefficients and lag obs
def predict(coef, history):
yhat = coef[0]
for i in range(1, len(coef)):
yhat += coef[i] * history[-i]
return yhat
X = pd.DataFrame()
for column in columns:
X[column] = difference(series[column].values)
size = (4*24)*54 # hoping
train, test = X[0:size], X[size:size+(14*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()] # https://stackoverflow.com/questions/20209600/panda-dataframe-remove-constant-column
#print(train.var(), X.info())
# train autoregression
model = VARMAX(train)
model_fit = model.fit(method='powell', disp=False)
#print(model_fit.mle_retvals)
##window = model_fit.k_ar
coef = model_fit.params
# walk forward over time steps in test
history = [train.iloc[i] for i in range(len(train))]
predictions = list()
for t in range(len(test)):
yhat = predict(coef, history)
obs = test.iloc[t]
predictions.append(yhat)
history.append(obs)
print(mean_squared_error(test, predictions))
0.5594208989876831
来自scikitlearn的那个mean_squared_error并不令人恐惧（实际上，它大约是文档中显示的三个样本的中间）。 _可能意味着数据是可预测的。我想在情节中看到它。
# plot
plt.plot(test)
plt.plot(predictions, color='red')
plt.show()
因此，这里发生的部分情况是数据是季节性的，因此必须对其应用平稳性。现在，这些线都是垂直的，而不是时间的。
但是让我担心的另一件事是红色数据的规模。太多了无论如何，我该如何反转平稳性并将日期重新应用于绘制数据？显然不应该那样。 :)
最佳答案
事实证明，首先将其制作为数据框：
predDf = pd.DataFrame(predictions)
关于python - ARIMA模型的平稳性反转，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/52525734/